AI Whitepaper 2025

 

A White Paper for Vocational Rehabilitation Policy Makers

December 2025

 Table of Contents Download PDF Version

Contributions from: Roberto Cordero, Yang-Su Cho, Laura Ozios-Townsend, John Sheahan, Mario Eiland, Tyson Ernst, Donna Elkins, and Zachary Abernathy
Additional contributions by: LaDell Lockwood, Gil Cupat, and Ashley Douthett

 

Table of Contents

Executive Summary

Go to Executive Summary

Chapter 1: The Disability Employment Gap

• Understanding the Current Employment Landscape
• Blind and Low Vision Employment Challenges
• Vocational Rehabilitation Program Outcomes
• Research-Identified Barriers to Employment

Go to Chapter 1

Chapter 2: Understanding Artificial Intelligence

• What is Artificial Intelligence?
• How AI Systems Learn
• AI in the Employment Context

Go to Chapter 2

Chapter 3: Legal Framework and Policy Landscape

• Federal Laws Governing AI in Employment
• EEOC Guidance on AI and Disability
• Evolving Landscape: The 2025 Regulatory Environment
• State-Level AI Legislation
• Implications for VR Practice

Go to Chapter 3

Chapter 4: AI Barriers to Disability Employment

• Résumé Screening and Algorithmic Bias
• Video Interview Discrimination
• Legal Developments and Employer Accountability
• State Regulatory Developments
• Implications for VR Counselors

Go to Chapter 4

Chapter 5: AI Assistive Technologies for Employment

• Visual Assistance Applications
• Smart Glasses and Wearable AI
• Mobility and Navigation Technologies
• Screen Readers and AI Enhancements
• Workplace Accommodation Technologies

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Chapter 6: AI Tools for Job Seekers with Disabilities

• Career Assessment and Exploration
• Résumé and Application Support
• Interview Preparation 
• Workplace Productivity Tools
• Professional Development Resources

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Chapter 7: Building AI Literacy for Competitive Employment

• The AI Skills Imperative
• Core AI Competencies for Employment
• AI Literacy Training Resources
• Practical AI Skills for Job Seekers
• Implications for VR Service Delivery

Go to Chapter 7

Chapter 8: Employment Service Models and AI Integration

• Pre-Employment Transition Services
• Supported Employment
• Customized Employment
• Self-Employment and Entrepreneurship
• AI-Enhanced Assistive Technology Training

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Chapter 9: Employer Engagement Strategies

• The Business Case for Disability Inclusion
• Addressing Employer Concerns About AI Hiring Tools
• Addressing Employer Concerns About Accommodation Costs
• Employer Education Resources
• Developing Employer Partnerships

Go to Chapter 9

Chapter 10: Emerging Careers in AI

• AI Ethics and Policy Positions
• Prompt Engineering
• Accessibility Testing and Consulting
• Data Labeling and Quality Assurance
• AI Support and Training Specialists
• Navigating AI Careers with a Disability

Go to Chapter 10

Chapter 11: Synthesis and Implementation Roadmap for Washington State DSB

• From Analysis to Action
• Summary of Critical Findings
• Strategic Priorities for Washingotn State DSB
• Implementation Timeline Overview
• Measuring Progress
• Conclusion

Go to Chapter 11

Conclusion

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Questions posed to employees at Department of Services for the Blind in Seattle

• Questions posed to LaDell Lockwood, Communications Manager
• Questions posed to Gil Cupat, Vocational Rehabilitation Counselor
• Questions posed to Ashley Douthett, Business Relations Specialist 

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References

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Appendix A: Comprehensive Glossary

Terms, acronyms, and technologies from all 11 chapters, alphabetized within subsections.

• A.1 Acronyms and Abbreviations 
• A.2 Artificial Intelligence and Technology Terms
• A.3 Screen Readers and Text-to-Speech Technologies 
• A.4 Visual Recognition and Description Technologies 
• A.5 Navigation and Mobility Technologies  
• A.6 Smart Glasses Platforms
• A.7 Generative AI Assistants 
• A.8 Productivity and Executive Function Tools  
• A.9 Résumé and Job Search Tools  
• A.10 AI Video Interview Platforms  
• A.11 Employment and Vocational Rehabilitation Terms  

Go to Appendix A

Appendix B:  Legal and Regulatory Framework 

Federal statutes, state legislation, executive orders, case law, and regulatory guidance governing AI in employment.

• B.1 Federal Statutes
• B.2 State Legislation
• B.3 Federal Executive Orders
• B.4 Case Law and Administrative Proceedings
• B.5 EEOC Guidance (Historical Reference)
• B.6 Washington State AI Task Force

Go to Appendix B

Appendix C: Employment and Workforce Statistics

Key statistics verified against original sources as of December 2025.

• C.1 General Disability Employment Statistics (2024)  
• C.2 Visual Impairment Employment Statistics  
• C.3 Work-Limiting Conditions Statistics (2024)  
• C.4 AI Workforce Projections  
• C.5 AI Training and Workforce Readiness  
• C.6 Disability Inclusion Business Case  
• C.7 Accommodation Cost Statistics  
• C.8 PEAT AI Resources Usage (2023–2024)  
• C.9 DSB Implementation Metrics Framework  

Go to Appendix C

Appendix D: Consolidated Resource Directory  

Organizations, training programs, and support services alphabetized within subsections.

• D.1 Federal Government Resources  
• D.2 National Employer Engagement Resources  
• D.3 Blindness and Low Vision Organizations  
• D.4 Washington State Resources  
• D.5 AI Learning and Training Resources  
• D.6 Financial Empowerment  
• D.7 Legal and Advocacy Resources

Go to Appendix D

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Executive Summary

Artificial intelligence is rapidly changing the employment landscape, offering new opportunities and challenges for people with disabilities. For example, Maria, a visually impaired job seeker, was rejected by an AI résumé screening system that misread her adaptive work history, underscoring the urgent need for fair integration. Vocational rehabilitation leaders must act to ensure AI benefits all. This paper reviews current impacts, identifies challenges, and provides actionable guidance drawn from research, statistics, legal updates, and advances in assistive technology.

Key questions guide the discussion: How does the disability employment gap impact policy? What AI concepts support employment strategies? Which AI-related barriers exist, and how can they be addressed? How do assistive AI tools aid employment? Which evidence-based models advance disability employment? What strategies support employer engagement for AI-driven inclusion?

The disability employment gap is persistent: only 22.7% of people with disabilities are employed, compared to 65.5% without disabilities. People with vision disabilities have even lower participation and higher unemployment (see Appendix C).

AI-powered hiring tools create both opportunities and challenges. Although promoted as objective alternatives to human bias, algorithms can still discriminate in specific, identifiable ways. For example, Chen, Z. (2023) found that specific AI systems declined qualified applicants based on patterns in their data. Wilson and Caliskan (2024) showed that AI résumé screening systems reviewed a set of résumés and, in 85.1% of cases, selected those with white-associated names over identical résumés with Black-associated names. Glazko et al. (2024) examined AI résumé rankings and found that résumés listing disability honors or credentials were consistently ranked lower than otherwise identical résumés. 

The ongoing Mobley v. Workday, Inc. lawsuit (No. 3:23-cv-00770-RFL, N.D. Cal.) alleges that AI used in hiring systems filters out candidates based on age, race, and disability. In July 2024, a federal court allowed the case to proceed as an example of potential AI discrimination. Measures such as AI training data audits and the bias audit requirements discussed later in this paper help address these issues by identifying and mitigating algorithmic bias, promoting fairer hiring practices.

AI has enabled assistive technologies that can improve employment outcomes. Screen readers such as JAWS 2025 now include AI-powered assistants to help users navigate complex software (Freedom Scientific, 2024). New mobility tools such as the Glidance Glide (Glidance, 2024) and smart glasses from Envision (Envision, 2024) and Meta (Meta, 2024) deliver real-time environmental information. Generative AI helps job seekers create accessible résumés, prepare for interviews, and build workplace skills (Henneborn, 2023). See Appendix A for a complete directory of AI-assistive applications.

DSB leaders must act now to leverage AI to advance disability employment and mitigate bias. Adopt AI literacy, require bias audits, implement targeted interventions, and proactively build partnerships with employers. Each chapter details practical actions that can be taken for immediate impact. Appendices provide technical resources to support next steps. 

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Chapter 1: The Disability Employment Gap

Understanding the Current Employment Landscape

The employment gap between people with and without disabilities persists in the U.S. Despite progress in rights and technology, people with disabilities remain underrepresented in competitive employment. Understanding this gap is crucial to assessing AI's potential to worsen or reduce disparities.

The employment-population ratio is 22.7% for people with disabilities and 65.5% for others (U.S. Bureau of Labor Statistics, 2025b). Only 27.1% of those with work-limiting health conditions are in the labor force, compared with 74.7% without such conditions.

Unemployment rates are higher for those with work-limiting conditions: 10.5% compared to 4.3%, nearly one-third of employed people with such conditions requested or made accommodations. In Canada, the disability employment gap decreased by 2.5 points between 2023 and 2025, now at 38.8 points (Hardy & Vergara, 2025). These statistics offer benchmarks for Washington State.

Blind and Low Vision Employment Challenges

People who are blind or have low vision face especially significant employment barriers. Labor force participation is about 44% among people with vision impairments, compared with 77% among people without disabilities. The 2024 unemployment rate for those with vision difficulty was 10%, compared with 4% for those without.

A common misconception requires clarification: The widely cited figure that 70% of blind people are unemployed is inaccurate. The National Research & Training Center on Blindness and Low Vision (2024) reported that only 8.1% of people with vision impairments are unemployed, based on 2023 American Community Survey data. Confusion arises from conflating non-participation in the labor force with unemployment. Over half of working-age individuals who are blind or visually impaired are out of the labor market, meaning they are neither working nor actively seeking work (American Foundation for the Blind, 2024b). This distinction suggests different intervention strategies are needed to encourage labor force participation or support active job seekers.

In addition to lower employment rates, people with vision disabilities face an annual earnings gap: sighted people earn about $11,215 more per year than their blind or low-vision peers, with higher gaps in some locations. They are also twice as likely to lack home internet access, which limits remote work and job searches (Reuschel et al., 2023).

Vocational Rehabilitation Program Outcomes

The Rehabilitation Services Administration (2025) reports that state VR programs served 818,646 participants during the July 2021 to June 2023 cohort period. Of these participants, 617,018 received career services, and 230,835 received training services. During the cohort period from July 2022 to June 2023, 48.7% of participants achieved measurable skill gains, an essential indicator of the program's effectiveness in preparing clients for competitive employment.

Research on specific populations reveals variable outcomes that inform service delivery strategies. A 2024 study published in the Journal of Rehabilitation found that 49% of young adults on the autism spectrum who exited VR services were employed, compared to 44% of young adults with other types of disabilities (Shenk et al., 2024).

Educational institutions were the most common source of referral for autistic young adults, highlighting the importance of transition partnerships between VR agencies and schools. Despite these successes, challenges remain in implementing the VR program. As of March 2024, 31 of 78 VR agencies were placed on corrective action plans to address performance deficiencies related to pre-employment transition services reserve requirements and timeliness of eligibility and Individualized Plan for Employment development.

States also relinquished $2.45 million in Supported Employment program funds during the FY 2023 reallocation period, indicating underutilization of resources specifically designated for individuals with the most significant disabilities (U.S. Department of Education, 2025).

Research-Identified Barriers to Employment

Extensive research has identified multiple barriers to employment for people with disabilities. A systematic review by Nagtegaal et al. (2023) identified 32 distinct factors affecting employers' hiring decisions for people with disabilities. These factors encompass capabilities (job candidates' skills and abilities), opportunities (job availability and accommodations), and motivations (employers' willingness and attitudes). This suggests that disability employment results from a complex decision-making process that requires multifaceted intervention strategies. See Appendix D for a comprehensive summary of research-identified barriers.

The most frequently cited barrier, appearing in 14% of factor mentions across the reviewed studies, is employers' belief that people with disabilities are less productive (Accenture, 2018; Disability: IN, 2024). This perception persists despite substantial evidence to the contrary, including research showing that employees with disabilities often demonstrate superior attendance, lower turnover, and comparable or higher productivity than their non-disabled peers (Accenture, 2018). The second most common barrier, cited in 11% of factor mentions, involves employer expectations about accommodation costs (Job Accommodation Network, 2024). This concern is often unfounded; the Job Accommodation Network (2024) reports that most workplace accommodations cost under $500, and many cost nothing.

Research at Mississippi State University has uncovered another critical barrier: implicit bias (McDonnall & Antonelli, 2018). Studies found that employers tend to automatically associate competence with sighted people and incompetence with blind people, even when their explicit attitudes appear favorable. This implicit bias operates below conscious awareness but significantly affects hiring decisions.

Subsequent research (McDonnall & Antonelli, 2022) demonstrated that implicit attitudes can be changed through targeted interventions, suggesting pathways for employer education programs. More positive self-reported employer attitudes are associated with a higher likelihood of hiring a person with a disability, suggesting that awareness training and positive exposure experiences may help address this barrier.

Individual factors also correlate with employment outcomes for people with disabilities. Research consistently shows that education level positively affects employment; people who are blind with a bachelor's degree or higher are employed at 65%, compared to 24% for those with less than a high school education (McDonnall & Tatch, 2021).

Early work experience is significant for youth with vision loss; those with at least two work experiences in high school are almost twice as likely to be employed as adults as those with no work experience (McDonnall, 2011). For individuals who read braille weekly and use a white cane, the likelihood of employment and higher earnings increases significantly (Bell & Mino, 2015). These findings underscore the importance of VR services that develop both educational credentials and practical skills during transition years. (Frentzel et al., 2021)

Implications for Vocational Rehabilitation

These statistics and research findings underscore the critical importance of VR services while highlighting areas for improvement. The persistent employment gap indicates that traditional approaches, while valuable, have not been sufficient to achieve equitable employment outcomes. To address these challenges, VR agencies should focus on specific strategies: leveraging employer education to combat algorithmic biases in hiring, directing assistive technology (AT) funding to ensure clients have access to the most effective AI-powered tools, and enhancing skills training to boost AI literacy and employability.

AI technologies present both new challenges in the form of algorithmic bias in hiring and new opportunities through assistive technology and skills training that VR agencies must strategically address. (Buyl et al., 2022) The following chapters examine these AI-related challenges and opportunities in detail, guiding VR administrators seeking to improve employment outcomes for clients with disabilities in an increasingly AI-mediated labor market.

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Chapter 2: Understanding Artificial Intelligence

What Is Artificial Intelligence?

Artificial intelligence refers to computer systems designed to perform tasks that typically require human intelligence, such as recognizing patterns, understanding language, making decisions, and solving problems. Unlike traditional software that follows explicit programming rules, AI systems learn from data to improve their performance over time.

For VR professionals, understanding AI is essential because these technologies increasingly affect both the employment landscape clients navigate and the assistive tools available to support them. (Touzet, 2023) As AI systems become integral to recruitment and operational processes, policymakers must assess their immediate impact on employment equity and inclusion when making future AI procurement decisions. (Pew Research Center, 2023) See Appendix A for definitions of technical AI-related terms.

AI is not a single technology but rather a collection of approaches that enable computers to perform tasks associated with human cognition. The field has evolved dramatically since its origins in the 1950s, with recent advances in computing power and data availability enabling capabilities that seemed like science fiction just a decade ago. Today's AI systems can describe images in natural language, carry on extended conversations, write computer code, and make predictions based on patterns too complex for humans to discern manually. (Tang et al., 2023)

How AI Systems Learn

Machine learning underpins most modern AI systems. Rather than being explicitly programmed with rules for every possible situation, machine learning systems identify patterns in large datasets and use them to predict or make decisions about new data they have not previously encountered. For example, a spam filter learns from millions of emails labeled as spam or not spam, identifying characteristics that distinguish unwanted messages. Over time, the system improves its accuracy as it processes more examples and receives feedback on its decisions.

This learning approach has profound implications for disability employment. (Fabeyo, 2025) When AI hiring tools are trained on historical hiring data from companies that have underemployed people with disabilities, the systems may learn to perpetuate those same biases. Employment gaps common among people with disabilities — due to health management, rehabilitation, or difficulty finding accessible employment — may be interpreted by AI systems as negative signals, even though they have no bearing on a candidate's ability to perform the job. Non-standard career paths, alternative credentials, and adaptive work histories may be devalued by systems trained primarily on data from non-disabled workers (U.S. Department of Justice, 2022).

Deep learning represents a more advanced form of machine learning that uses layered neural networks to process complex information. These computational structures, modeled on principles of neural organization, excel at tasks such as image recognition, speech processing, and natural language understanding — capabilities particularly relevant to assistive technology. Deep learning powers many AI assistive technologies used by people with vision disabilities, including image description features in apps like Seeing AI (Microsoft, 2017) and Be My Eyes (Be My Eyes, 2023), speech recognition in screen readers, and object detection in navigation tools.

Understanding that these systems improve through exposure to diverse data helps explain why some tools perform better than others and why user feedback matters for ongoing development. (Parasurama & Ipeirotis, 2025)

Large language models represent the most recent significant advance in AI capabilities. Systems like ChatGPT, Claude, and Gemini are trained on vast amounts of text data from the internet and other sources, enabling them to generate human-like responses to questions, create written content, summarize documents, translate between languages, and assist with a wide range of language-based tasks.

For job seekers with disabilities, these tools offer powerful capabilities for résumé writing, interview preparation, and professional communication. (Canadian Council on Rehabilitation and Work, 2024) VR counselors can help clients leverage these tools while understanding their limitations, including the risk of biased outputs stemming from their training data. (Alliance Enterprises, 2024)

AI in the Employment Context

AI technologies now touch virtually every stage of the employment lifecycle. In recruiting, AI-powered job boards match candidates to positions by analyzing skills, experience, and other factors extracted from résumés and profiles.

During initial screening, applicant tracking systems use AI to filter résumés before any human reviewer sees them, often eliminating most applications based on keyword matching and pattern recognition. Assessment increasingly involves AI analysis of video interviews, game-based evaluations, and skills tests that aim to predict job performance from behavioral and linguistic signals.

Once hired, employees encounter AI in onboarding systems that use chatbots and adaptive learning to help new workers become proficient. Performance monitoring increasingly relies on AI to track productivity, communication patterns, and work outputs. Professional development is shaped by AI-powered learning platforms that create personalized training pathways based on identified skill gaps and career goals (AgentiveAIQ, 2025).

The scale of AI adoption in hiring is striking. According to Jobscan (2024), 492 of the Fortune 500 companies now use applicant tracking systems to streamline recruitment and hiring (Aitechtonic, 2025; Jobscan, 2025). This widespread adoption means that VR clients must navigate AI-mediated hiring processes regardless of the specific jobs or industries they pursue. Understanding how these systems work, their potential biases, and strategies for presenting oneself effectively to both AI and human reviewers has become an essential component of job readiness for all job seekers, including those with disabilities (U.S. Department of Justice, 2022).

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Chapter 3: Legal Framework and Policy Landscape

Federal Laws Governing AI in Employment

Federal anti-discrimination laws provide the foundation for addressing AI bias in hiring, though their application to algorithmic decision-making continues to evolve through regulatory guidance and litigation. VR counselors must understand these laws to help clients exercise their rights and to inform employer engagement efforts. See Appendix B for a comprehensive summary of relevant federal and state laws.

The Americans with Disabilities Act of 1990 (ADA) prohibits discrimination against qualified individuals with disabilities in all aspects of employment, including hiring. Title I applies to employers with 15 or more employees and requires reasonable accommodations unless doing so would cause undue hardship. Under the ADA, employers may not use selection criteria that screen out, or tend to screen out individuals with disabilities unless the requirements are job-related and consistent with business necessity.

This standard applies whether discrimination occurs through human decisions or algorithmic systems. When AI hiring tools systematically disadvantage applicants with disabilities, whether through biased training data, inaccessible interfaces, or criteria that serve as proxies for disability status, employers may face liability under the ADA.

To help ensure compliance and foster inclusivity, agencies could consider implementing an ADA compliance checklist for their AI hiring tools. Questions to consider: Does the tool offer an option to request accommodations? How often is the tool audited for bias? Are stakeholders with disabilities involved in the tool’s development or assessment? A proactive approach to reviewing AI systems can provide reassurance that these tools enhance, rather than hinder, equitable hiring practices.

Section 503 of the Rehabilitation Act of 1973 requires federal contractors with contracts exceeding $10,000 to take affirmative action to employ individuals with disabilities. Contractors with 50 or more employees and contracts of $50,000 or more must develop written affirmative action programs with specific utilization goals.

The Office of Federal Contract Compliance Programs (OFCCP) enforces these requirements, which impose additional obligations beyond those under the ADA. Federal contractors using AI hiring tools must ensure these systems support rather than undermine their affirmative action obligations.

Title VII of the Civil Rights Act of 1964 prohibits employment discrimination based on race, color, religion, sex, or national origin. While Title VII does not directly address disability, it establishes crucial precedents for algorithmic discrimination claims through its disparate impact framework. Under this framework, facially neutral employment practices that disproportionately affect protected groups may constitute illegal discrimination unless the employer can demonstrate the practice is job-related and consistent with business necessity. Courts and regulators are increasingly applying this framework to AI hiring tools that produce discriminatory outcomes.

The Age Discrimination in Employment Act of 1967 (ADEA) prohibits discrimination against individuals aged 40 and older. This law is particularly relevant to AI hiring tools, as several high-profile cases have involved age-based algorithmic discrimination. The EEOC v. iTutorGroup settlement (2023), discussed below, established that automated age-based screening violates the ADEA regardless of whether humans review the filtered results.

EEOC Guidance on AI and Disability

The U.S. Equal Employment Opportunity Commission (EEOC) issued technical assistance in May 2022 specifically addressing the ADA and the use of software, algorithms, and AI to assess job applicants and employees (EEOC, 2022). This guidance clarified that employers may be liable for disability discrimination when AI hiring tools screen out individuals with disabilities, even when the tools are developed or administered by third-party vendors. The guidance established that employers cannot delegate their ADA obligations to software vendors and must ensure that any tools they use comply with anti-discrimination requirements.

The EEOC guidance identified three primary ways AI hiring tools may violate the ADA. First, an employer may fail to provide reasonable accommodation that allows an applicant to be evaluated by the AI tool — for example, when a video interview platform lacks captioning for deaf applicants or when a timed assessment disadvantages applicants with cognitive disabilities. Second, AI tools may screen out applicants based on disability-related characteristics that are not job-related, such as penalizing employment gaps related to medical treatment or flagging atypical speech patterns during video interviews. Third, employers using AI tools may make impermissible disability-related inquiries before extending a conditional job offer, for example, when assessment tools probe for information about medical conditions or treatment history.

The EEOC guidance was removed from the agency's website on January 27, 2025, following Executive Order 14179. However, the ADA's underlying statutory requirements remain entirely in effect. Employers continue to face liability for discriminatory AI hiring practices regardless of changes in regulatory guidance. VR counselors should advise clients that their ADA rights apply to AI-mediated hiring processes, and that employers should be informed that removing guidance does not change their legal obligations.

Evolving Landscape: The 2025 Regulatory Environment

The regulatory landscape for AI in employment has shifted significantly in 2025, creating both challenges and opportunities for disability employment advocates. Understanding these changes helps VR professionals navigate a complex and evolving environment.

Executive Order 14179, signed January 23, 2025, revoked the Biden administration's comprehensive AI executive order (Executive Order 14110) and directed federal agencies to remove AI-related guidance documents. As noted above, this led to the removal of EEOC technical assistance on AI and disability.

Executive Order 14281, signed April 23, 2025, further directed federal agencies to deprioritize disparate-impact enforcement theories and established an AI Litigation Task Force to challenge state AI discrimination laws.

Most recently, Executive Order 14365, signed December 11, 2025, intensified federal opposition to state-level AI regulation. These federal policy changes do not alter the underlying statutory prohibitions on disability discrimination. The ADA, ADEA, Title VII, and Section 503 remain in effect, and employers using AI hiring tools that produce discriminatory outcomes continue to face potential liability.

Private litigation and state enforcement may become more significant as federal enforcement priorities shift. VR professionals should help clients understand that their legal rights remain intact even as the regulatory environment evolves.

State-Level AI Legislation

Several states have enacted or proposed legislation specifically addressing AI discrimination in employment, creating a patchwork of requirements that employers must navigate. Washington State has emerged as a leader in AI governance, with implications for both VR service delivery and employer engagement.

Washington State's Engrossed Second Substitute Senate Bill 5838 (E2SSB 5838), signed March 18, 2024, established the Washington State Artificial Intelligence Task Force to examine AI development and deployment across sectors, including employment (Washington State Legislature, 2024). The Task Force released its inaugural report on December 30, 2024, and continues to develop policy recommendations.

Governor Jay Inslee's Executive Order 24-01, issued February 1, 2024, directed state agencies to establish AI governance frameworks that ensure accessibility and equity (Inslee, 2024). The Division of Vocational Rehabilitation's Knowledge Interpreter chatbot, launched in 2024, demonstrates how state agencies can implement AI responsibly to improve service delivery.

California's amendments to the Fair Employment and Housing Act (FEHA), effective October 1, 2025, establish employer liability for discriminatory outcomes arising from automated decision systems in employment, regardless of vendor responsibility (California Civil Rights Council, 2025). The regulations require employers to ensure that AI assessments are accessible to applicants with disabilities and prohibit the use of AI tools that have not been validated for fairness.

Colorado's Artificial Intelligence Act (SB 24-205), initially scheduled for February 1, 2026, was delayed to June 30, 2026, by SB 25B-004 (signed August 28, 2025). The law requires developers and deployers of high-risk AI systems to use reasonable care to avoid algorithmic discrimination. Employment decisions are explicitly included as high-risk applications, and the law establishes impact assessment requirements and a private right of action for affected individuals.

Illinois HB 3773, effective January 1, 2026, amends the Illinois Human Rights Act to address AI discrimination in employment. The law prohibits employers from using AI systems that have a discriminatory effect based on protected characteristics and requires applicants to be notified when AI is used in hiring decisions.

Implications for VR Practice

The legal and policy landscape for AI in employment creates both obligations and opportunities for VR agencies. Counselors should help clients understand their rights to reasonable accommodation in AI-mediated hiring processes, including the right to request human-administered alternatives to inaccessible assessments.

Documenting AI-related barriers that clients encounter can support both individual advocacy and systemic change efforts. Employer engagement should include education on the benefits and opportunities of inclusive hiring practices, as well as the legal aspects of AI hiring tools (U.S. Department of Justice, 2022).

While some employers may be unaware of their responsibilities regarding vendor-supplied AI systems, emphasizing the potential to enhance their workforce with diverse talent can be more compelling. VR professionals can position themselves as partners to help employers not only comply with applicable laws but also expand their talent pools to include highly qualified candidates with disabilities.

By framing inclusion as an opportunity for competitive advantage, employers may be more motivated to adopt responsible hiring practices (Partnership on Employment & Accessible Technology [PEAT], 2024; U.S. Department of Labor, Office of Disability Employment Policy, 2024a).

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Chapter 4: AI Barriers to Disability Employment

Résumé Screening and Algorithmic Bias

Applicant tracking systems (ATS) and AI-powered résumé screening tools represent the first significant barrier many job seekers with disabilities encounter. These systems, used by most large employers, automatically filter applications before any human reviewer sees them. Research has documented multiple ways these tools systematically disadvantage applicants with disabilities.

Wilson & Caliskan (2024) conducted a systematic test of AI résumé screening systems and found significant racial bias: 85.1% of cases favored résumés with white-sounding names over identical résumés with Black-sounding names. While this research focused on racial prejudice, similar mechanisms can disadvantage applicants with disabilities when AI systems are trained on historically biased data or use criteria that correlate with disability status.

Glazko et al. (2024) specifically examined disability bias in large language models used for résumé screening. Their research found that ChatGPT-4 consistently ranked résumés lower when they included disability-related credentials, honors, or experiences. The model penalized membership in disability-related organizations, awards recognizing disability advocacy, and employment with disability-focused nonprofits. This bias operated even when the disability-related content demonstrated relevant skills and leadership experience. The researchers found that explicit instructions to avoid disability bias reduced but did not eliminate discriminatory rankings.

Employment gaps present particular challenges for applicants with disabilities. Many people with disabilities experience periods out of the workforce due to health management, rehabilitation, or difficulty finding accessible employment. AI screening systems often interpret gaps negatively, flagging applications for rejection without considering the reasons for discontinuous employment (U.S. Department of Justice, 2022).

Research by Sheard (2025) found that AI systems trained to identify "job hoppers" or screen for "stability" systematically disadvantaged applicants whose employment histories reflected disability-related circumstances.

Video Interview Discrimination

AI-powered video interview platforms analyze candidates' facial expressions, vocal patterns, eye contact, and word choices to assess personality traits, emotional intelligence, and predicted job performance. These systems create significant barriers for applicants with disabilities whose communication patterns differ from the neurotypical norms on which the systems are trained.

Applicants who are deaf or hard of hearing may be disadvantaged by systems that analyze vocal patterns or penalize the use of sign language interpreters. Applicants with autism or other neurodevelopmental conditions may receive lower scores for atypical eye contact, facial expressions, or speech patterns that differ from neurotypical norms.

Applicants with speech disabilities may be flagged negatively by systems analyzing vocal qualities. Applicants with anxiety disorders may be penalized for nervous behaviors that do not affect job performance (American Civil Liberties Union, 2025; U.S. Department of Justice, 2022).

The March 2025 administrative complaint filed by the ACLU against Intuit and HireVue highlights these concerns (HR Dive, 2025; Public Justice, 2025). The complaint, filed on behalf of D.K., a Deaf Indigenous woman, alleges that HireVue's AI video interview software discriminated against her during the application process for a tax expert position at Intuit. According to the complaint, the AI system could not properly evaluate her responses because of her Deaf communication style and reliance on American Sign Language. Both HireVue and Intuit have publicly denied the allegations, and the investigation by the Colorado Civil Rights Division and EEOC is ongoing as of December 2025.

Research has documented that automated speech recognition systems perform significantly worse for deaf or hard-of-hearing speakers and for speakers with accents (Glasser, 2019). When these flawed transcriptions are used for candidate evaluation, qualified applicants may be screened out due to technological limitations rather than their actual qualifications.

Legal Developments and Employer Accountability

The legal landscape for AI hiring discrimination continues to evolve through litigation and regulatory action, establishing important precedents for employer accountability. The EEOC v. iTutorGroup settlement (EEOC, 2023), finalized in August 2023, marked the first federal AI hiring discrimination case. The company's software automatically rejected female applicants over age 55 and male applicants over age 60. The $365,000 settlement and required practice changes established that automated screening systems violate anti-discrimination laws regardless of human oversight. The case demonstrated that "the computer did it" is not a defense against discrimination claims.

Mobley v. Workday, Inc. (No. 3:23-cv-00770-RFL, N.D. Cal.) represents the most significant ongoing AI hiring discrimination litigation. Filed in February 2023, the case alleges that Workday's AI-powered applicant screening software discriminates against applicants based on race, age, and disability. In May 2025, Judge Rita F. Lin granted collective certification under the ADEA, allowing the case to proceed on behalf of applicants age 40 and older who were rejected since September 2020.

Court filings revealed that Workday processed approximately 1.1 billion applications during this period. In July 2025, the court expanded the collective's scope to include individuals processed using HiredScore AI features that Workday had acquired. Discovery is ongoing, with class certification on race and disability claims expected in 2026.

State Regulatory Developments

State legislatures have moved to address AI discrimination in hiring, where federal action has stalled. These developments create new compliance obligations for employers and new protections for job seekers.

California's FEHA regulations, effective October 1, 2025, establish comprehensive requirements for AI hiring tools. Employers must ensure that automated decision systems do not discriminate based on protected characteristics, including disability. The regulations require that AI assessments be accessible to applicants with disabilities and establish employer liability, regardless of whether tools are developed in-house or purchased from vendors.

The Colorado Artificial Intelligence Act, effective June 30, 2026 (delayed from February 1, 2026), establishes a reasonable care standard for high-risk AI systems, including those used in employment decisions. Developers must provide documentation of algorithmic discrimination testing, while deployers must conduct impact assessments and implement risk management practices. The law provides a private right of action for individuals harmed by algorithmic discrimination.

Illinois HB 3773, effective January 1, 2026, requires employers to notify applicants when AI is used in hiring decisions and prohibits AI systems that produce discriminatory effects. The law amends existing human rights protections to address algorithmic discrimination explicitly.

Implications for VR Counselors

VR counselors should help clients develop strategies for navigating AI-mediated hiring processes while advocating for systemic change. To optimize résumés, counselors can teach clients to use keywords from job descriptions, format résumés for ATS compatibility, and present employment gaps strategically without misrepresenting their histories.

Tools like Jobscan can help clients test their résumés against specific job postings to identify optimization opportunities (Jobscan, 2024). For accommodation requests, clients should understand their right to request alternatives to AI assessments under the ADA. This may include requesting human-administered interviews instead of AI video analysis, extended time on timed assessments, or alternative formats for inaccessible application components. Counselors should help clients document accommodation requests and responses (U.S. Department of Justice, 1993).

For documentation purposes, VR agencies should systematically track the AI-related barriers clients encounter. This documentation can support individual advocacy efforts, inform policy recommendations, and contribute to the evidence base for regulatory action (Aboulafia & Claypool, 2025).

For employer engagement, VR professionals can help employers understand their legal liability for discriminatory AI tools and connect them with resources for bias auditing and accessible hiring practices. The Partnership on Employment & Accessible Technology (PEAT) provides extensive resources on AI and disability inclusion that can support these conversations (PEAT, 2024).

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Chapter 5: AI Assistive Technologies for Employment

Visual Assistance Applications

AI-powered visual assistance applications have transformed information access for people who are blind or have low vision. These tools use computer vision and natural language processing to describe visual content, read text, and identify objects in real time. For employment purposes, these applications help users navigate workplaces, access visual information, and perform job tasks that would otherwise require sighted assistance (Be My Eyes, 2023a).

Seeing AI, developed by Microsoft (2017), provides a comprehensive suite of visual assistance features. The app describes people, including their approximate age, gender, and emotion; reads both printed and handwritten text; identifies products by scanning barcodes; describes scenes and environments; recognizes currency denominations; and provides light detection for users with some light perception. The app is free and available for iOS, making it accessible to many job seekers and employees.

Be My Eyes has evolved from a volunteer-based visual assistance platform to an AI-powered solution. The Be My AI feature, launched in 2023 in partnership with OpenAI, uses GPT-4 to provide instant image descriptions without waiting for a volunteer connection (Be My Eyes, 2024). Users can take a photo and receive detailed descriptions of documents, products, environments, or any visual content. The AI can answer follow-up questions about images, enabling more interactive information gathering than static descriptions alone.

Envision AI offers both a mobile application and a smart glasses platform for text reading, scene description, and object identification (Envision, 2024). The Envision app includes specialized features for document scanning, handwriting recognition, and batch processing of multiple images. The smart glasses platform, discussed below, extends these capabilities to hands-free use in employment settings.

Speakaboo, released in 2024, takes a "question-first" approach to visual assistance (Speakaboo, 2024). Rather than providing generic image descriptions, the app prompts users to specify what information they need before capturing an image. This targeted approach can be more efficient for specific workplace tasks in which users know precisely what information they need to extract from visual content. The app is free on iOS.

Smart Glasses and Wearable AI

Smart glasses are an emerging category of AI-assistive technology that provides hands-free access to visual information. These devices are particularly relevant for employment contexts where users need to interact with their environment while receiving AI assistance (Enabler.ai, 2025).

Envision's Ally Solos Glasses, announced for October 2025 shipment, are standalone smart glasses designed for people who are blind or have low vision (Envision, 2025). Priced at $399-$599, the glasses provide AI-assisted visual assistance without requiring a smartphone connection. Features include text reading, scene description, object identification, and navigation assistance. The standalone design addresses a limitation of earlier smart glasses that required smartphone tethering.

Ray-Ban Meta Smart Glasses gained AI visual assistance capabilities through the Live AI feature announced in December 2024 (Meta, 2024). Users can ask the glasses questions about what they see and receive real-time audio responses—the UK expansion in April 2025 extended availability beyond the initial US market. While not explicitly designed as assistive technology, the mainstream consumer positioning and relatively affordable price point (compared to specialized assistive devices) make these glasses potentially accessible to more users.

Mobility and Navigation Technologies

AI-powered mobility technologies extend beyond traditional white canes and guide dogs, providing environmental information and navigation assistance. These tools can support employment by helping users navigate unfamiliar workplaces, business travel, and outdoor environments between work locations (Smith & McKeever, 2023).

Glidance Glide is an AI-powered robotic mobility device designed for independent navigation by people who are blind (Glidance, 2024). The device, which won the SXSW 2025 Pitch Competition in the HealthTech/Accessibility category, uses sensors and AI to detect obstacles and guide users through environments. Initially announced for delivery in fall 2025, production was delayed to spring 2026. The device represents a new category of mobility assistance between traditional canes and complete robotic guides.

WeWalk Smart Cane 2 integrates AI-powered navigation with a traditional white cane form factor (WeWalk, 2024). The cane provides obstacle detection above waist level (complementing the ground-level detection of the cane tip), voice assistant integration for navigation and information queries, and smartphone connectivity for GPS navigation. The device won the CES 2025 Engadget Best in Show award, recognizing its innovation in assistive technology.

Biped NOA is a wearable mobility device consisting of a vest with 3D cameras that provides obstacle detection up to 10 meters through haptic feedback (Biped, 2024). The hands-free design allows users to carry items or use other mobility aids while receiving environmental information. The device is particularly suited to complex environments such as busy workplaces or public spaces.

OKO AI Copilot for the Blind is a mobile app that recognizes pedestrian "walk" and "don't walk" signals and replicates the chirping sounds of crosswalks to enhance crossing safety (AYES Inc., 2024). The app addresses a specific but essential safety need for navigation between work locations in urban environments.

Screen Readers and AI Enhancements

Screen readers remain essential assistive technology for computer access, and recent AI enhancements have significantly expanded their capabilities for employment tasks (American Printing House for the Blind, 2023). JAWS 2025, released October 30, 2024, introduced the FS Companion AI feature (Freedom Scientific, 2024). This integration provides AI-powered assistance for describing images, summarizing documents, answering questions about on-screen content, and providing contextual help for complex applications. The AI assistant operates within the familiar JAWS environment, reducing the learning curve for users already proficient with JAWS. FS Companion can describe charts, graphs, and visual elements in documents that were previously inaccessible without sighted assistance.

NVDA 2025.1, the free open-source screen reader, introduced the Remote Access feature, enabling remote technical support and collaboration (NV Access, 2025). While not an AI feature per se, this capability supports employment by allowing coworkers or IT support staff to assist blind employees remotely. NVDA's user base exceeds 250,000, making it a significant platform for assistive technology access (NV Access, 2025b).

Workplace Accommodation Technologies

AI-powered workplace accommodation tools help employees with disabilities perform job tasks more effectively, and employers meet their accommodation obligations more efficiently (U.S. Department of Labor, 2024b). Salesforce's partnership with Inclusively led to the Retain platform, which uses AI to match employees’ accommodation needs with appropriate solutions (Salesforce, 2024). The system helps employers identify adequate accommodation while protecting employee privacy by focusing on functional needs rather than specific diagnoses.

Cephable provides computer interaction for people with motor disabilities using AI-powered speech recognition, facial expression tracking, and head movement controls (Cephable, 2024). The system uses offline AI processing for privacy and responsiveness, enabling users to control computers without a traditional mouse and keyboard. Employment applications include any computer-based work tasks.

Vision Buddy is a headset that streams live television with magnification features while maintaining ambient awareness (Vision Buddy, 2024). For employment contexts, the device can be used for video conferencing, training videos, and other work-related video content.

VizLens is a mobile app that assists users with complex interfaces, such as control panels, by allowing them to take a photo and hear descriptions of buttons and controls (Guo et al., 2016). Workplace applications include operating equipment, accessing building controls, and navigating unfamiliar technology interfaces.

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Chapter 6: AI Tools for Job Seekers with Disabilities

Career Assessment and Exploration

AI-powered career assessment tools help job seekers identify suitable career paths based on their interests, skills, and abilities. For people with disabilities, these tools can help identify careers that align with their strengths while considering accessibility requirements (VocRehabTools, 2025).

The O*NET Interest Profiler, available through the National Center for O*NET Development (2025), assesses work-related interests and matches them with occupations. While not generative AI, the system uses sophisticated algorithms to analyze responses and generate personalized career suggestions. VR counselors can use this tool with clients to systematically explore career options.

LinkedIn's AI features include job recommendations based on profile analysis, skill assessments with personalized feedback, and career path suggestions based on the trajectories of similar professionals (LinkedIn, 2025). The platform's AI can identify skills gaps and suggest training resources to improve competitiveness for target positions.

Indeed's career tools include AI-powered résumé analysis, salary comparison tools, and job-matching algorithms (Indeed, 2025). The platform's accessibility features include screen reader compatibility and alternative application methods for inaccessible employer systems.

Jobscan uses AI to compare résumés against specific job descriptions, identifying keyword matches and optimization opportunities (Jobscan, 2024). This tool helps job seekers understand how ATS systems evaluate their applications and make targeted improvements.

Résumé and Application Support

Generative AI tools have transformed résumé writing and application preparation, providing capabilities that can particularly benefit job seekers with disabilities who may have non-traditional career paths or need assistance with written communication (Milne, 2024a).

Google Docs' AI features, including writing suggestions, grammar correction, and formatting assistance, can help job seekers create professional application materials (Google, 2024). The platform's accessibility features make these tools available to users of screen readers and other assistive technologies.

Grammarly provides AI-powered writing assistance, including grammar checking, clarity suggestions, and tone analysis (Grammarly, 2024). Premium features include full-sentence rewrites and professional formatting suggestions. The tool can be invaluable for job seekers who need support with written communication or who are writing in a second language.

ChatGPT, Claude, and Gemini can assist with résumé writing, cover letter drafting, and application responses. These large language models can help job seekers articulate their experiences, translate skills across industries, and tailor applications to specific positions. VR counselors should help clients understand both the capabilities and limitations of these tools, including the importance of reviewing and personalizing AI-generated content.

Interview Preparation

AI tools can help job seekers prepare for interviews through practice, feedback, and research assistance. (Daryanto et al., 2024) AI interview coaches provide practice interview questions with feedback on responses. Tools range from simple question generators to platforms that analyze video responses and provide detailed coaching. Job seekers should be aware that practicing with AI interview tools is different from being evaluated by the AI interview systems employers use, and strategies appropriate for human interviewers may differ from those needed for algorithmic evaluation (EEOC, 2023).

Large language models can help job seekers research companies, prepare responses to common questions, and develop questions to ask interviewers. These tools can also help job seekers prepare to discuss accommodation needs by suggesting professional language and anticipating employer questions.

Workplace Productivity Tools

AI-powered productivity tools can support employees with disabilities in performing job tasks effectively (Touzet, 2023). Goblin Tools provides AI-powered task breakdown, prioritization, and time estimation designed for neurodivergent users (Goblin Tools, 2024). The Magic ToDo feature breaks complex tasks into manageable steps, while other tools help with emotional regulation and communication. These tools can support employment success for people with ADHD, autism, and other conditions affecting executive function.

Otter.ai provides AI-powered transcription and meeting notes with speaker identification (Otter.ai, 2025). The service can support employees who are deaf or hard of hearing by providing real-time meeting transcription and benefit other employees by creating searchable records of meetings and conversations.

Speechify converts written content to natural-sounding audio (Speechify, 2024). For employees with reading disabilities, visual impairments, or those who process audio information more effectively than written text, Speechify can improve access to workplace documents and training materials.

Tiimo is a visual daily planner designed for users with ADHD and autism (Tiimo, 2024). The app provides visual schedules, routines, and reminders that can support workplace time management and task completion.

Professional Development Resources

AI and digital resources support ongoing professional development for workers with disabilities. Microsoft Learn offers free AI and accessibility training with certifications (Microsoft, 2024). The platform includes courses on AI fundamentals, specific AI tools, and accessibility implementation, helping workers with disabilities develop in-demand skills.

The National Federation of the Blind (2024) provides resources and programs to support career development for blind individuals, including mentoring networks, training programs, and advocacy initiatives.

AFB CareerConnect, operated by the American Foundation for the Blind (2024), provides career guidance, job-search resources, and mentoring for people with vision loss. Hadley offers free distance-learning courses for people with vision loss (Hadley, 2024), including employment-related topics such as technology skills, job-search strategies, and workplace success.

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Chapter 7: Building AI Literacy for Competitive Employment

The AI Skills Imperative

Artificial intelligence literacy has become a foundational requirement for competitive employment across industries (Artificial Intelligence Literacy Act, 2023). The World Economic Forum's Future of Jobs Report 2025 projects that AI will create 170 million jobs globally while displacing 92 million, resulting in a net increase of 78 million jobs by 2030 (World Economic Forum, 2025). However, 39% of workers' core skills are expected to change during this period, requiring substantial workforce adaptation. For people with disabilities, developing AI literacy can provide a competitive advantage while helping them navigate AI-mediated hiring processes and workplace environments (Randstad, 2024b).

LinkedIn's Work Change Report (2025) documents the accelerating demand for AI skills. Demand for AI literacy skills has increased more than sixfold in the past year, yet only 1 in 500 current job listings explicitly require them, suggesting that AI competence is becoming an assumed baseline rather than a specialized qualification. More than 50% of hiring managers report that candidates who cannot demonstrate AI literacy will not be hired for roles where AI could enhance productivity (Kimbrough, 2025). This creates both opportunity and risk for job seekers with disabilities: those who develop AI skills gain competitive advantages, while those without these skills face growing barriers.

The AI training gap presents a particular concern. (Phutane et al., 2025) According to Randstad's November 2024 survey, only 35% of workers received AI training from their employers (Randstad, 2024). The training gap shows concerning demographic patterns: 22% of baby boomers received AI training, compared to 45% of Generation Z workers. People with disabilities, who already face employment barriers, may be disproportionately excluded from workplace AI training opportunities, making VR-provided training particularly important. (Phutane et al., 2025)

Core AI Competencies for Employment

AI literacy for employment encompasses several interconnected competencies that VR programs should address. These competencies range from basic awareness to practical application skills. Foundational AI understanding includes knowing what AI is and is not, how AI systems learn from data, basic concepts of machine learning and large language models, and awareness of AI capabilities and limitations. This foundation helps workers evaluate AI tools critically and understand when AI assistance is appropriate.

Prompt engineering – the skill of communicating effectively with AI systems – has emerged as a valuable competency across job functions. Workers who can formulate clear, specific prompts get better results from AI tools, making them more productive and valuable to employers. This skill transfers across different AI systems and applications.

AI tool evaluation involves assessing AI tools for usefulness, reliability, accessibility, and appropriateness for specific tasks. Workers need to understand that not all AI tools are equally capable or trustworthy, and that selecting appropriate tools for specific tasks is a skill in itself (Allen et al., 2025).

Data literacy encompasses understanding how data shapes AI outputs, recognizing potential biases in training data, and evaluating the reliability of AI-generated information. This competency is particularly important given documented biases in AI systems that can affect people with disabilities. Critical thinking about AI involves recognizing AI limitations, verifying AI outputs, understanding when human judgment should override AI recommendations, and identifying potential harms from AI use. These skills protect workers from over-relying on flawed AI systems while enabling them to capture legitimate benefits.

AI Literacy Training Resources

Multiple free and low-cost resources support the development of AI literacy among job seekers and workers with disabilities. Elements of AI, developed by the University of Helsinki and Reaktor (Elements of AI, 2024), offers a free introductory course on AI fundamentals accessible to non-technical learners. The course covers what AI is, machine learning basics, neural networks, and AI ethics. The web-based format is accessible to screen reader users.

AI for Everyone, developed by Andrew Ng and available through Coursera (2019), provides a non-technical introduction to AI for business professionals. The course explains AI capabilities and limitations, helps learners identify AI opportunities in their work, and discusses AI ethics and societal implications.

Microsoft Learn (2024) offers free AI training modules with certifications. The platform includes both introductory content and specialized courses on specific AI tools and applications. Accessibility features support learners using assistive technologies.

Podcasts provide accessible AI education for auditory learners. The Lex Fridman Podcast features in-depth interviews with AI researchers and industry leaders. 80,000 Hours explores AI's impact on careers and society. Data Skeptic provides accessible explanations of AI and data science concepts.

Practical AI Skills for Job Seekers

Beyond conceptual understanding, job seekers benefit from practical skills with specific AI applications relevant to employment. Generative AI for professional communication includes using tools such as ChatGPT, Claude, and Gemini to draft emails, create documents, summarize information, and prepare for professional interactions. Job seekers should practice techniques to develop prompts that return useful outputs and develop skills for reviewing and editing AI-generated content.

AI-assisted research involves using AI tools to research companies, industries, and job markets; synthesize information from multiple sources; and identify trends and opportunities. These skills support both job searching and workplace performance.

AI productivity tools vary by industry and function, but common applications include AI-powered writing assistance, meeting transcription and summarization, task management and automation, and data analysis support. VR programs should help clients identify AI tools relevant to their career goals and develop proficiency with accessible options.

AI for accessibility includes using AI tools to overcome disability-related barriers, for example, using image description AI to access visual content, using transcription services to access audio content, or using text-to-speech for reading support. Developing expertise with these tools can improve both job performance and quality of life (U.S. Department of Justice, 2022).

Implications for VR Service Delivery

VR agencies should integrate AI literacy into service delivery across program areas. For transition-age youth, Pre-Employment Transition Services should include AI awareness and foundational skills as part of job exploration and workplace readiness training. The World Economic Forum's projection that 22% of jobs will experience significant disruption by 2030 means that today's students will enter a workforce substantially shaped by AI (World Economic Forum, 2025a).

Career counseling should help clients understand how AI affects their target industries and develop relevant AI competencies. Counselors themselves need sufficient AI literacy to guide clients effectively, suggesting a need for staff development in this area (Rahman et al., 2023). Skills training programs should incorporate AI tools appropriate to specific career paths. This includes both general-purpose AI applications for productivity and specialized AI tools used in specific industries or functions.

Job placement services should prepare clients to discuss AI competencies in interviews and demonstrate relevant skills to employers. As hiring managers increasingly expect AI literacy, the ability to articulate AI experience is an effective job-seeking skill.

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Chapter 8: Employment Service Models and AI Integration

Pre-Employment Transition Services

Pre-Employment Transition Services (Pre-ETS) provide early intervention for students with disabilities to prepare them for post-secondary education and employment. Under the Workforce Innovation and Opportunity Act (WIOA), state VR agencies must reserve at least 15% of their federal funds for Pre-ETS, serving students ages 14-22 who are eligible or potentially eligible for VR services. AI literacy and assistive technology training should be integrated across Pre-ETS components.

Job exploration counseling should include information on AI's impact on career fields, helping students understand how different occupations are being transformed by AI and which skills will be needed (Workforce Innovation Technical Assistance Center, 2024). Career assessment tools can help identify aptitudes and interests, while AI-powered research tools can help students systematically explore career options.

Work-based learning experiences should include exposure to AI tools used in workplace settings. Students should have opportunities to observe and practice with AI applications relevant to their career interests, developing familiarity before entering competitive employment (Frentzel et al., 2021). Counseling on post-secondary opportunities should address AI-related education and training programs. Students should understand how AI is being incorporated into educational programs across fields and what AI competencies they should develop during post-secondary education.

Workplace readiness training should include AI literacy as a foundational component. Students should graduate from Pre-ETS programs with a basic understanding of AI, proficiency in prompt engineering, and familiarity with AI tools relevant to their career goals (Washington State Department of Social and Health Services [DSHS], 2025b). Self-advocacy instruction should prepare students to advocate for accessible AI tools and accommodations related to AI-mediated processes. Students should understand their rights regarding AI in hiring and the workplace (U.S. Department of Justice, 2022).

Supported Employment

Supported Employment provides intensive services for individuals with the most significant disabilities to achieve competitive integrated employment. The model emphasizes rapid job placement followed by intensive on-the-job support, with services tailored to individual needs and workplace demands. AI technologies create opportunities to enhance the effectiveness of supported employment (OECD, 2023).

Job development can be enhanced by AI tools that identify employer contacts, research company cultures, and match client strengths with employer needs. Job developers can use AI to prepare customized employer presentations and track outreach activities more systematically.

Job coaching can incorporate AI assistive technologies as natural supports. Rather than providing ongoing human assistance for specific tasks, job coaches can train clients and employers to use AI tools that provide the needed support. For example, AI image description might reduce the need for sighted assistance with visual tasks, while AI task management tools might reduce the need for ongoing prompting support.

Long-term support services can leverage AI tools to maintain employment success after intensive services end. AI productivity tools, scheduling applications, and communication support can help employees with significant disabilities sustain employment as human support levels decrease.

Wehman et al. (2018) documented the effectiveness of supported employment for individuals with significant disabilities, finding that the model produces superior employment outcomes compared to sheltered or segregated settings. AI tools offer opportunities to extend the model's reach by providing natural supports that reduce the intensity of human assistance needed while maintaining employment success (VocRehabTools, 2025).

Customized Employment

Customized employment develops individualized job opportunities for job seekers with significant disabilities that are matched to employer needs. The approach may include job carving, self-employment, and micro-enterprises. AI technologies offer new possibilities for customized employment development and success (Riesen et al., 2022).

Discovery processes can incorporate AI tools for systematic assessment and documentation. AI-powered assessments may reveal strengths and interests that traditional approaches miss, while AI documentation tools can help counselors capture and organize discovery information more effectively.

Job carving negotiations can be informed by AI analysis of job tasks and workflows. AI tools can help identify which job functions might be carved out or combined to create positions that match candidate capabilities while meeting employer needs.

Self-employment and micro-enterprise support can leverage AI business tools. Entrepreneurs with disabilities can use AI for business planning, marketing, customer communication, bookkeeping, and other functions that might otherwise require hiring assistance or remain undone. This can improve the viability of small business ventures for people with disabilities.

Self-Employment and Entrepreneurship

Self-employment represents an important pathway to economic independence for people with disabilities, offering flexibility in work arrangements and the ability to design accessible work environments. AI tools are transforming small business operations in ways that can benefit entrepreneurs with disabilities.

The National Disability Institute (2022) reports that approximately 1.8 million business owners in the United States have disabilities. The Randolph-Sheppard Business Enterprise Program provides entrepreneurship opportunities for individuals who are blind to operate food service facilities on federal and other properties. According to data from the Rehabilitation Services Administration, the program generated $747 million in gross receipts in FY2023, with 1,428 blind vendors operating facilities nationwide.

AI business tools can support entrepreneurs with disabilities across multiple functions. AI writing tools can help with marketing content, customer communications, and business correspondence. AI accounting tools can simplify bookkeeping and financial management. AI scheduling tools can manage appointments and coordinate with customers. AI customer service tools can handle routine inquiries, extending the reach of solo entrepreneurs (Mukherjee, 2025).

VR agencies should consider how AI tools can make self-employment more viable for clients who might not otherwise have the capacity to manage all aspects of a small business independently (DSHS, 2023; VocRehabTools, 2025).

AI-Enhanced Assistive Technology Training

Assistive technology training should be integrated across all employment service models, with particular attention to AI-enhanced tools that can improve employment outcomes (DSHS, 2023).

The assessment should identify both barriers that AI-assistive technologies might address and AI tools that align with client preferences and capabilities. Not all clients will benefit from the same AI tools, and individual assessment should inform technology recommendations.

Training should develop proficiency with selected AI assistive technologies, including both device operation and strategic application to employment tasks. Training should address not just how to use tools but when and why to use them effectively. Follow-up should ensure that AI assistive technologies continue to meet employment needs as job requirements and technologies evolve.   Regular check-ins can identify needs for additional training, tool updates, or alternative solutions.

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Chapter 9: Employer Engagement Strategies

The Business Case for Disability Inclusion

Research consistently demonstrates that disability-inclusive hiring practices benefit employers by improving workforce quality, reducing turnover, enhancing innovation, and broadening market understanding. VR professionals can use this evidence to engage employers in disability hiring initiatives that incorporate AI accessibility.

Accenture's research on disability inclusion has produced compelling evidence of a business case. Their 2018 study found that companies identified as "Disability Inclusion Champions" achieved, on average, 28% higher revenue, twice the net income, and 30% higher profit margins than their peers over four years (Accenture, 2018).

Updated 2023 research found that these advantages have grown, with Disability Inclusion Champions now achieving 1.6x higher revenue and 2.6x higher net income compared to other companies (Accenture, 2023).

These findings reflect advantages in talent acquisition, customer loyalty, and innovation that come from inclusive practices.

Despite this evidence, disability inclusion remains underdeveloped at most companies. The 2024 Disability Equality Index report from Disability: IN found that only 11% of Fortune 500 companies participating in the index have board members who openly identify as having a disability (Disability: IN, 2024).

This leadership gap indicates significant room for improvement in disability inclusion strategies, including AI hiring practices.

Addressing Employer Concerns About AI Hiring Tools

Employers increasingly recognize the legal and reputational risks of AI-related hiring discrimination, creating opportunities for VR professionals to position themselves as resources for inclusive AI practices (Nugent & Scott-Parker, 2022). Legal liability concerns have intensified following high-profile cases and regulatory developments. The EEOC v. iTutorGroup settlement and ongoing Mobley v. Workday litigation demonstrate that employers face real consequences for discriminatory AI tools. State regulations in California, Colorado, and Illinois have created additional compliance requirements. VR professionals can help employers understand these risks and identify resources for addressing them.

The EEOC's 2022 technical guidance on AI and disability — while removed from the agency website in January 2025 — established principles that remain relevant to employer compliance. Employers cannot delegate ADA obligations to software vendors. Employers must provide reasonable accommodations for AI-administered assessments. Employers are liable for discriminatory screening outcomes regardless of intent. VR professionals can help employers understand these continuing obligations.

Bias audit resources are available to help employers evaluate AI hiring tools. The Partnership on Employment & Accessible Technology (PEAT) provides extensive resources on AI and disability inclusion, including guidance on bias auditing and the implementation of accessible AI. PEAT's AI resources were accessed over 130,000 times by more than 54,000 unique users from 2023 to mid-2024 (PEAT, 2024), indicating strong employer interest in this topic.

Addressing Employer Concerns About Accommodation Costs

Employer concerns about accommodation costs remain a significant barrier to hiring people with disabilities, despite evidence that these concerns are largely unfounded (Job Accommodation Network, 2024a). VR professionals should proactively address cost concerns with accurate information.

The Job Accommodation Network (2024a) reports that approximately 56-61% of workplace accommodations cost nothing, involving changes to policies, schedules, or work processes rather than purchases.

For accommodations that do require investment, the median one-time cost is approximately $300. These figures contrast sharply with employer perceptions that accommodations are expensive. AI assistive technologies may reduce accommodation costs by providing natural supports that decrease the need for human assistance or specialized equipment.

For example, AI image description may reduce the need for sighted assistance with visual tasks, while AI scheduling tools may reduce the need for job coaching support (Be My Eyes, 2023). VR professionals can help employers understand how AI tools can serve as cost-effective accommodation solutions (VocRehabTools, 2025).

Employer Education Resources

Multiple national resources support employer education on disability inclusion and accessible AI practices. The Employer Assistance and Resource Network on Disability Inclusion (EARN) provides comprehensive resources on recruiting, hiring, and retaining employees with disabilities (EARN, 2024). EARN resources address AI hiring tools and accessibility considerations, helping employers implement inclusive practices.

The Job Accommodation Network (JAN) offers free, confidential guidance on workplace accommodations and ADA compliance (JAN, 2024b). JAN consultants can help employers identify adequate accommodations, including AI-based solutions, for specific job functions and disability types.

The Workforce Recruitment Program (WRP) connects students and recent graduates with disabilities to federal internship and employment opportunities. The program demonstrates federal employer commitment to disability hiring and can serve as a model for other employers.

PEAT's AI and disability inclusion resources help employers understand how AI affects workers with disabilities and how to implement AI tools in accessible ways (PEAT, 2024). Resources include toolkits, case studies, and guidance on bias auditing.

Developing Employer Partnerships

VR agencies should develop strategic employer partnerships that address AI-related barriers and opportunities. Effective partnerships go beyond individual job placements to create systemic improvements in employer practices.

Initial outreach should assess employer AI hiring practices and identify opportunities for improvement. Questions to explore include what AI tools the employer uses in hiring, whether those tools have been evaluated for accessibility and bias, what accommodation processes exist for AI-administered assessments, and what training hiring managers have received on AI and disability.

Partnership development should include education on AI hiring risks and resources. Employers may be unaware of bias audit requirements, accessibility obligations, or available resources for improving practices. VR professionals can provide valuable guidance as they build relationships that lead to hiring opportunities.

Ongoing collaboration should address both individual placements and systemic improvements. As employers implement accessible AI practices, they become better partners for placing multiple candidates. VR agencies can provide feedback on candidate experiences that help employers continue improving.

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Chapter 10: Emerging Careers in AI

The rapid expansion of artificial intelligence technologies has created entirely new career pathways that did not exist a decade ago. (Mullens & Shen, 2025) For people with disabilities and other personal circumstances, these emerging roles offer unique opportunities to leverage lived experience as a professional asset while contributing to the development of more equitable AI systems (U.S. Department of Justice, 2022).

This chapter examines six emerging AI career areas, analyzes their accessibility characteristics, identifies relevant skills and qualifications, and provides guidance for vocational rehabilitation (VR) professionals supporting clients in pursuing these opportunities. Each career pathway discussed represents a field in which disability experience can provide a competitive advantage rather than pose barriers to entry.

AI Ethics and Policy Positions

 As concerns about AI bias and discrimination grow, organizations are creating dedicated roles focusing on ethical AI development and deployment (Coursera, 2024; Teal HQ, 2025). These positions often welcome diverse perspectives, including those of people with disabilities who bring lived experience with how AI systems can fail to serve all users equitably.

AI ethics specialists review AI systems for bias and recommend mitigations, working with development teams to identify potential harm before products reach users. Policy analysts develop organizational and governmental AI regulations by translating technical considerations into governance frameworks. Compliance officers ensure AI systems meet legal and ethical requirements while monitoring for discrimination and other harms as regulations evolve (Second Talent, 2025).

These roles typically require strong analytical skills, communication abilities, and an understanding of both technology and its social impacts. Educational pathways commonly include backgrounds in philosophy, computer science, law, or public policy, though formal degrees are not always required (Coursera, 2024). People with disabilities who have navigated systems that failed to accommodate them frequently develop sharp analytical capabilities for identifying accessibility failures and bias.

VR counselors can help clients recognize how their experiences position them for these emerging roles and develop the additional skills needed to compete for them. The global AI governance market is projected to reach $3.7 billion by 2028, driven by increasing regulatory requirements and corporate responsibility initiatives (Second Talent, 2025; Grand View Research, 2024d).

Prompt Engineering

Prompt engineering – the skill of effectively communicating with AI systems to achieve desired outcomes – has emerged as a distinct professional role in addition to being a general workplace competency as discussed in Chapter 7 (Coursera, 2025; eWeek, 2024). Prompt engineers develop prompts that help AI systems produce accurate, useful outputs for specific applications. They test and refine prompts across different scenarios and user needs, document best practices, and train others in effective AI interaction.

The global prompt engineering market is projected to grow at a compound annual growth rate of 32.8% between 2024 and 2030 (Grand View Research, as cited in Coursera, 2025).

This role is accessible for people with strong communication and analytical skills, regardless of physical abilities. The work can often be performed remotely, providing flexibility that benefits many people with disabilities. While advanced programming skills are not required for many positions, candidates benefit from familiarity with AI language models and a basic understanding of machine learning concepts (Indeed, 2025). Strong language skills, attention to detail, and systematic thinking position candidates competitively, making this an accessible entry point to AI-adjacent careers for people from various backgrounds. VR agencies can help clients develop prompt engineering skills as part of AI literacy training and identify positions where these skills are valued.

Accessibility Testing and Consulting

People with disabilities bring invaluable expertise to AI accessibility testing roles that non-disabled testers cannot replicate (Accessible.org, 2024; DigitalA11Y, 2024). Testing AI applications for accessibility compliance and usability requires understanding not just technical standards but how real users with disabilities actually interact with technology.

Following the principle of "Nothing About Us Without Us" – a foundational concept in disability rights activism popularized by James Charlton's (1998) seminal work emphasizing that people with disabilities must be central participants in all decisions affecting their lives – organizations developing AI tools increasingly recognize that solutions designed without input from people with disabilities often fail to address real needs or create new barriers.

Accessibility testers evaluate AI applications against technical standards such as the Web Content Accessibility Guidelines (WCAG) and real-world usability criteria. They provide user feedback that improves AI tools not just for users with disabilities but for all users, as accessibility improvements often enhance overall usability. Consulting roles involve advising development teams on accessibility requirements during AI development, helping prevent issues that can be costly to fix financially, reputationally, and legally after release.

These roles leverage disability-related expertise as a professional asset rather than treating disability as a limitation to be accommodated (Rehabilitation Services Administration, 2024). The 2024 regulatory updates to Title II of the Americans with Disabilities Act and Section 504 of the Rehabilitation Act are creating thousands of new accessibility positions across government, education, and private sectors (Accessible.org, 2024).

Data Labeling and Quality Assurance

AI systems require large amounts of labeled data for training, creating demand for data annotation work that can often be performed remotely with appropriate accommodations (LabelVisor, 2024; RWS, 2025). Data labelers identify and tag elements in images, text, audio, or video to create training datasets that teach AI systems to recognize patterns.

Quality assurance specialists review AI outputs to ensure accuracy and appropriateness, catching errors before they reach end users. The data annotation market is projected to reach $8.22 billion by 2028, reflecting sustained demand for human oversight in AI training processes (LabelVisor, 2024; IMARC Group, 2025).

Specialization in accessibility-related annotation represents a particularly valuable niche (U.S. Department of Justice, 2022). Creating high-quality image descriptions for training visual AI, evaluating AI-generated alt text for accuracy, or assessing whether AI outputs meet accessibility standards requires expertise that people with disabilities can develop through their own experiences with assistive technology and accessibility challenges. Major platforms such as DataAnnotation.tech, RWS TrainAI, and LXT offer remote, flexible work arrangements with hourly rates ranging from $15 to $70, depending on specialization and experience level (ZipRecruiter, 2025).

These roles offer entry points into AI industry careers that leverage disability-related expertise while providing flexible schedules that accommodate various disability-related needs.

AI Support and Training Specialists

Building on the data-related roles discussed above, as organizations adopt AI tools, they need specialists to support implementation and train users. AI support specialists help employees learn to use AI tools effectively, troubleshoot issues, and develop training materials and documentation. Supporting inclusive AI adoption for employees with disabilities is a growing need as organizations seek to ensure AI tools enhance, rather than impede, workplace accessibility (U.S. Department of Labor, 2024a).

These roles combine technical knowledge with communication and training skills, often a good fit for people with disabilities who have experience explaining accessibility concepts and training others to use assistive technology. Career pathways into these roles typically include backgrounds in technical support, training and development, or disability services, with additional learning about specific AI tools and platforms. VR counselors can help clients recognize how skills developed through managing their own disability-related technology needs translate to professional AI support roles. Organizations increasingly value candidates who can proactively address accessibility considerations, making disability experience a distinctive Qualification. (Phutane et al., 2025)

Navigating AI Careers with a Disability

When pursuing AI-related careers, job seekers with disabilities should understand specific considerations that affect the application and hiring process. AI video interview analysis systems may disadvantage applicants whose communication patterns differ from the norms of the training data (HR Dive, 2025; Public Justice, 2025). Research has documented that automated speech recognition systems perform worse for deaf or hard-of-hearing speakers and speakers with accents, potentially screening out qualified candidates based on disability-related characteristics rather than job qualifications. A March 2025 complaint filed by the ACLU against Intuit and HireVue alleged that AI-backed video-interview software discriminated against a deaf and Indigenous employee, highlighting ongoing concerns about these technologies (HR Dive, 2025).

As discussed in Chapters 3 and 4, candidates should request human-administered alternatives when possible, exercising their rights under the Americans with Disabilities Act (ADA) to reasonable Accommodations in hiring processes. The U.S. Equal Employment Opportunity Commission (EEOC, 2022) has issued technical guidance clarifying that the ADA applies to AI-based hiring tools and that employers must provide reasonable accommodations for applicants who cannot participate in AI-administered assessments on a fair basis.

The U.S. Department of Justice (2025) guidance on algorithms and disability discrimination further reinforces that employers remain liable for discriminatory outcomes from AI hiring tools, even when developed by third-party vendors. Understanding that these accommodation requests are legally protected helps job seekers advocate for themselves confidently.

Building demonstrable skills through portfolios, projects, and certifications can help bypass biased screening systems that might filter out candidates based on résumé characteristics unrelated to actual qualifications. Employers increasingly value demonstrated capabilities over credentials, and portfolios of AI-related work provide concrete evidence that screening algorithms cannot easily dismiss (Bone et al., 2023). Job seekers should recognize that lived experience with accessibility challenges constitutes a competitive advantage for many AI roles. Organizations are increasingly seeking diverse perspectives in AI development, and disability experience provides insights that improve products for all users – insights that candidates without such experience cannot offer (Stanford Institute for Human-Centered Artificial Intelligence, 2025).

As AI technologies continue to reshape the employment landscape, VR professionals who understand these emerging opportunities will be positioned to help clients navigate an evolving career ecosystem where diverse perspectives are increasingly valued.

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Chapter 11: Synthesis and Implementation Roadmap for Washington State DSB

From Analysis to Action

The preceding chapters have examined how artificial intelligence is fundamentally reshaping the employment landscape for people with disabilities. The analysis reveals a complex picture: AI simultaneously creates unprecedented barriers through biased hiring systems while offering transformative assistive technologies that can substantially improve employment outcomes (U.S. Department of Justice, 2022). For Washington State's Department of Services for the Blind (DSB), this dual nature of AI demands a strategic response that mitigates risks while leveraging opportunities (Washington State Department of Social and Health Services, 2025a). This chapter synthesizes key findings into an actionable implementation roadmap aligned with Washington's policy environment, resource capacity, and leadership in AI governance.

Summary of Critical Findings

The disability employment gap remains substantial and persistent. Bureau of Labor Statistics (BLS, 2024) data show that in 2023, only 22.5% of people with disabilities were employed, compared to 65.8% of people without disabilities, representing a 43.3 percentage point gap. For people with vision disabilities, BLS supplemental data indicate an employment-population ratio of approximately 43.7%, with an unemployment rate more than double that of people without vision disabilities (BLS, 2024, Table A-6). These disparities persist despite decades of policy effort and represent both a significant challenge and an untapped talent pool that Washington employers need amid ongoing workforce shortages.

AI hiring tools present documented risks of discrimination. (Phutane et al., 2025)** Research demonstrates that AI résumé screening systems exhibit bias against applicants with disabilities, people of color, women, and older workers (Raghavan et al., 2020; Dastin, 2018). The Mobley v. Workday litigation and the Equal Employment Opportunity Commission (EEOC) ‘s landmark iTutorGroup settlement establish that these risks carry legal consequences for employers (EEOC, 2023).

Video interview analysis platforms systematically disadvantage applicants whose communication patterns differ from training data norms, creating particular barriers for job seekers who are deaf or hard of hearing, neurodivergent, or have speech differences.

AI assistive technologies offer transformative potential. Screen readers now include AI-powered assistants that provide contextual help. Smart glasses provide real-time access to visual information. Mobility devices provide autonomous guidance.

Generative AI tools help job seekers create professional application materials and prepare for interviews. These technologies can meaningfully reduce disability-related barriers when job seekers have access to them and training to use them effectively.

The workforce is transforming rapidly. The World Economic Forum (WEF) projects that 39% of workers' core skills will change by 2030 (WEF, 2025). AI and big data skills are the fastest-growing competencies. LinkedIn's most recent Workplace Learning Report indicates that demand for AI literacy skills has increased more than sixfold, yet only 35% of workers received AI training during the reporting period (LinkedIn, 2024). Vocational rehabilitation (VR) clients who develop AI literacy skills gain competitive advantages in a job market where these skills are increasingly expected but inconsistently developed.

Washington State has established leadership in AI governance. (Ruckle, 2025)** The AI Task Force established by Engrossed Substitute Senate Bill 5838 (ESSB 5838; Washington State Legislature, 2024) has developed initial recommendations to shape AI governance, and continued policy development is anticipated through 2025 and beyond. Governor Inslee's Executive Order 24-01 on responsible AI use in state government (Inslee, 2024) and the Division of Vocational Rehabilitation's (DVR's) pioneering Knowledge Interpreter chatbot further position Washington to lead nationally in applying AI responsibly to improve disability employment outcomes. This leadership creates both opportunity and obligation to demonstrate effective practice.

Strategic Priorities for Washington State DSB

These findings point to five strategic priorities for Washington State's Department of Services for the Blind, each addressing critical dimensions of the AI-employment intersection.

Priority 1: Protect Clients from AI Hiring Discrimination. DSB counselors must prepare clients to navigate AI-mediated hiring processes while the agency simultaneously advocates for systemic change. This requires training counselors to recognize AI hiring barriers and to teach clients strategies for optimizing applications for applicant tracking systems without misrepresenting qualifications. Counselors should help clients understand their right to request accommodations in AI-administered assessments, including human-administered alternatives to video interviews. DSB should systematically document the AI-related barriers clients encounter to create an evidence base for policy advocacy. Partnerships with employers should include discussions of AI hiring tool auditing and accessible application processes. To quickly demonstrate success and build momentum, a specific focus on training 50% of the counselors within the first 90 days can serve as a visible win, showcasing our commitment to supporting clients effectively.

Priority 2: Expand Access to AI Assistive Technologies. AI-powered assistive technologies can substantially improve employment outcomes when clients have access to appropriate devices and practical training. DSB should inventory current assistive technology offerings and identify gaps in AI-enhanced options. Partnerships with assistive technology vendors should secure demonstration units for counselor familiarization and client evaluation. Training programs should incorporate instruction in AI-enhanced assistive technologies, connecting technology skills to specific employment goals. DSB should track employment outcomes for clients receiving AI-enhanced assistive technology services to build an evidence base for continued investment.

Priority 3: Build AI Literacy for Clients and Staff. AI literacy has become a baseline expectation for competitive employment, as documented in the workforce transformation findings above. DSB should develop tiered AI literacy training appropriate for different client populations and employment goals. Basic AI awareness should be incorporated into job-readiness programs for all clients. Advanced AI skills training should be available for clients pursuing careers where these skills are required or advantageous. Staff development should ensure counselors can effectively guide clients in developing AI skills and model appropriate use of AI tools.

Priority 4: Strengthen Employer Engagement Around Inclusive AI Practices**. Employers face legal liability and competitive disadvantages when AI hiring tools exclude qualified candidates with disabilities. DSB should position itself as a resource helping employers ensure their AI hiring tools are disability inclusive. This involves educating employers about the legal liability that attaches when AI tools discriminate, connecting employers with bias audit services, advocating for accessible application processes that do not rely solely on AI screening, and helping employers understand their legal obligation to provide alternative assessment options when requested as accommodations.

Priority 5: Contribute to Washington State AI Policy Development. Washington's AI Task Force has developed initial recommendations to shape AI governance, and continued policy development is expected through 2025 and beyond. DSB should actively contribute disability employment perspectives to this process, ensuring that AI policies protect job seekers with disabilities while enabling beneficial uses of AI. Staff should participate in AI Task Force proceedings that address disability employment implications. DSB should submit formal comments on proposed AI regulations, advocating for disability-inclusive requirements. Partnerships with disability advocacy organizations should amplify the voices of the disability community in AI policy development.

Implementation Timeline Overview

DSB should implement these strategic priorities according to the following phased timeline.

Immediate actions (0-3 months) include establishing an AI barrier documentation process, beginning AI Task Force engagement, developing client accommodation rights materials, completing the assistive technology inventory and gap analysis, and developing initial employer education materials.

Near-term actions (3-6 months) include deploying AI hiring navigation counselor training, establishing partnerships with assistive technology vendors, developing a tiered AI literacy curriculum, training employer engagement staff on AI issues, and preparing an AI policy position paper.

Medium-term actions (6-12 months) include piloting AI literacy training for Pre-Employment Transition Services (Pre-ETS) students, integrating AI-assistive technology training into existing programs, launching employer-inclusive AI initiatives, implementing assistive technology outcome tracking, and submitting AI Task Force comments.

Ongoing actions include documenting and analyzing AI-related barriers, updating training as technologies evolve, expanding employer partnerships, contributing to policy development, and evaluating and refining approaches based on outcomes.

Measuring Progress

Each strategic priority should be tracked through specific performance indicators. See Appendix C for the complete metrics framework.

For **Priority 1** (Protecting Clients from AI Hiring Discrimination), key indicators include the number of AI-related barriers documented through client intake and case notes, the percentage of counselors who have completed AI hiring navigation training, the number of clients receiving AI accommodation guidance, and employment outcomes for clients who reported AI-related barriers.

To illustrate the impact of these metrics, consider the journey of a client, Sarah, who faces difficulties during the job application process because of inaccessible AI hiring systems. Through DSB's intervention, her encounters with AI barriers are consistently documented, enabling us to address her needs specifically. With her counselor, who is trained in AI hiring navigation, Sarah receives customized guidance on how to approach these challenges. She requests accommodations tailored to her situation and successfully navigates interviews, ultimately securing a role within a company that values inclusivity. Her success story exemplifies the positive outcomes these indicators can drive, making the data more relatable and inspiring stakeholders to maintain diligent tracking.

Priority 2 (Expanding Access to AI Assistive Technologies) metrics include assistive technology inventory completeness, including AI-enhanced options; the number of vendor partnerships established; the number of clients receiving AI-enhanced assistive technology services; and employment outcomes for assistive technology service recipients.

Priority 3 (Building AI Literacy) performance measures include the number of training modules developed across proficiency levels, client completion rates by training level, counselor completion of AI-focused professional development, and correlations between AI literacy level and employment outcomes.

Priority 4 (Strengthening Employer Engagement) indicators consist of the number of employers receiving AI hiring education, employers conducting bias audits, employers implementing accessible alternatives to AI-only screening, and successful placements with engaged employer partners.

For **Priority 5** (Contributing to Policy Development), DSB should track AI Task Force proceedings attended, formal comments submitted on proposed regulations, policy recommendations that incorporate disability employment perspectives, and regulations adopted with disability-inclusive provisions.

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Conclusion

Artificial intelligence is neither inherently beneficial nor inherently harmful for disability employment. Its impact depends on how it is developed, deployed, and governed. Washington State's Department of Services for the Blind has the opportunity and the responsibility to shape that impact for the job seekers it serves. The five strategic priorities outlined in this chapter provide a framework for action. By protecting clients from AI discrimination, expanding access to AI assistive technologies, building AI literacy, strengthening employer engagement, and contributing to policy development, DSB can help ensure that AI advances rather than impedes disability employment outcomes.

Washington has already demonstrated leadership through the AI Task Force and Executive Order 24-01, while sister agency DVR's Knowledge Interpreter chatbot provides a model for AI implementation in vocational rehabilitation. With the foundation laid, the next step is systematic implementation that translates analysis into outcomes for the hundreds of thousands of Washingtonians with disabilities who deserve equitable access to employment in an AI-transformed economy. The convergence of Washington's policy leadership, evolving AI technologies, and persistent employment disparities makes this moment critical for decisive action.

Which single action can policymakers take tomorrow to ensure AI advances rather than impedes equity in disability employment? Are you willing to name it and commit to it? A direct challenge can refocus your attention on commitment, providing a starting point for meaningful change.

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Questions posed to employees at Department of Services for the Blind in Seattle:

Questions posed to LaDell Lockwood Communications Specialist:

Have you noticed AI being mentioned more often at job fairs or in public talks about jobs? What examples have you seen?

Yes! AI is talked about in every workshop, event, article, or LinkedIn post about finding employment. In the past, talk was about how AI could help in creation of resumes and cover letters. This has now morphed into the importance of knowing how to create prompts (for business personal use) and creating searches to find positions. 

Also, there is more conversation about the downfalls of AI in the job hunt, especially when it comes to resumes and cover letters. The technology still tends to “hallucinate” and may over-embellish content. It’s also not as creative as many think and provides the same answers and content to everyone who uses AI to create cover letters for the same or similar job. This not only keeps people from standing out from the crowd; it shows that the job seeker is using AI for the search, which may or may not be helpful.

This happened to me on an interview panel I was on. The first cover letter I read used an interesting turn of phrase that I had never heard before. I thought the applicant was very creative and could help with some Comms activities occasionally. However, the second cover letter I read used the exact same phrase lowering my “creativity score” for both applicants.

Has the rise of AI changed the way you plan or promote events like job fairs? If so, how?

It hasn’t seriously impacted my promo work yet. However, I know that AI searches are different than the online search engines we used (and optimized for) in the past. Now we will need to look at the background functions of parts of the website to make sure that our selling points are not hidden from the AI. That may lead to another revamp of the website (which would be bad).

Are there any AI tools—like ones for writing or handling data—that you wish you could use every day at work? How do you think they would help?

Right now, I’m still researching how AI could help in my work. I am not using any of the chat engines like ChatGPT. But AI is now built into many of the programs I use, and I let that functionality work for me. For example, Photoshop’s AI can now remove backgrounds with a click, something that used to take more time in user input. And most of the search engines now use AI so even basic search is an adventure in prompt writing.

So mostly I will use the AI provided through other programs to do things like outlining presentations and other materials. I’ll check what it can do with data when I have more detailed data to analyze.

Have you changed the way you talk about jobs or agency services because AI is now part of the job market?

Not really. I have always known and talked about the importance of technology in job hunting. AI is another tool in that toolbox. And users need to learn what it can and cannot do effectively.

Do you see any possible problems that AI could cause for job seekers who are blind or have low vision?

AI is being used to run fake job interviews, on both the employer and job seeker sides. Things job hunters need to be aware of is that some scammers will post a phony job, collect applicants, and run interviews to entice people to give up personal information. During online interviews, AI is used to create fake faces that to make the job seeker “more comfortable.” The use of these AI overlays is detected through visual means. For example, noticing a lag in voice vs. face movement. Also, a current giveaway of an AI overlay is placing one’s hand over their face to disrupt the AI. People with visual disabilities may be unable to notice these clues.

Do you think AI could help your agency reach more people or share information more easily?

I’m not sure yet. I think the agency needs to have clear strategic objectives to determine what tactics to employ. Then you can see if AI can help complete those tactics.

Questions to Gil Cupat Vocational Rehabilitation Counselor:

Have you noticed customers using things like resume builders, job search websites, or screen readers that use AI? What stood out to you about how they used these tools? 

I am not aware of any of my customers using AI-related websites unless websites like Indeed, LinkedIn or Monster have started incorporating AI in their own domain. I do have several software developers and IT customers whom I’m sure have used AI in some capacity. 

Do you think AI could make it easier for people who are blind or have low vision to find or keep a job? Why or why not? 

I believe AI is the wave of the future. It’s embedded in a lot of things we do and it’s another tool that our customers can incorporate in their quest to look for or retain jobs. The technology it brings will greatly enhance the accessibility features of the devices we dispense to our customers.

Are there any worries you have about how AI might affect people who are blind or have low vision when they are looking for work?

Privacy is the number concern our customers who when it comes to AI. The technology is rapidly evolving and our customers, especially the ones that are not computer savvy, feel that their information is out there for anyone to take advantage of.

Have customers shared any stories with you about problems or successes they’ve had with AI during their job search or when applying for jobs? 

The struggle or challenges many of our customers have involves applying online or going to different websites that are not fully accessible. AI can either improve accessibility or make it even more challenging depending on who’s managing the websites. AI forces job seekers, our customers included to invent ways to be creative and use verbiage in line with what AI screening parameters set for would-be applicants. This process creates unwanted discrimination against those job seekers who have less education, ESL applicants, senior citizens and other marginalized people who have minimal computer experience.

What do you think about the idea that AI might take away jobs or create new ones for people with disabilities? 

It’s slowly becoming a reality now. We’ve seen less grocery cashiers now and more self-checkouts. We have Amazon stores that are self-governing and AI-generated help lines. PWD and other marginalized populations will surely feel the brunt of this changing job landscape, partly because of the perception that this group is an expendable asset that can be easily “replaced” and keeping PWD in the workforce may result in unwanted expenditures. Of course, none of it is true but AI technology shrinks the job pie to even smaller slices which can result in a stiffer competition for job applicants vying for limited job opportunities in the future.

Do you think AI could help customers learn new skills or reach their job goals in the future? 

Yes, here at DSB, we believe that technology enhances our customers’ lives through accessibility, learning to be more independent and doing the work using the technology they learn and use. AI has its place and the future is now. We as an agency need to embrace and incorporate AI technology in the way we partner with our customers, vendors, and other agencies. It’s also having its downsize because it’s a non-feeling entity with no regards for feelings, situations, or disability. It will continue to march on and it’s up to us to either embrace its existence or learn to evolve with it.  AI is here to stay.

Questions posed to Ashley Douthett, Business Relations Specialist at DSB:

Have you noticed employers using AI when they hire people? What did you find interesting or different about it?

Recruiting has changed a lot within the last couple of years. There are so many AI programs and tools that can be utilized for things like writing job descriptions, outbound and inbound talent sourcing, and candidate engagement through chatbots.

I had not utilized AI when I was recruiting, but after doing more research, I can see the advantages and drawbacks for employers. I myself enjoyed the personal aspect of getting to know candidates through the application and interview process, and I know job seekers appreciate the human connection as well. Some AI tools could impede that connection.   

There are nuances that only humans can catch, such as the value of transferable skills. I know how to look at a well-written resume and dissect for the transferable skills that may add up to the required or preferred qualifications from the job description, whereas it may be more black and white for AI, which can eliminate a lot of strong applicants. 

Do you think AI makes it easier or harder for people who are blind or have low vision to get jobs?

This is a really tough one, because I can see how it could help and hurt. Some ideas in which it could help – getting ideas on what kinds of questions an employer may ask for this job description, or can you help me understand some of the key skills required for this job? Just as starting points, of course. Because a lot of these concepts are ones in which job seekers should be flexing those critical thinking skills to learn about.

Again, AI can be helpful as starting points for job seekers. JobScan is one that people like. It helps give an idea of how strong your resume is in comparison to the job description, but it’s not always accurate, and can further lead people into thinking they need to have a 100% match in order to consider applying, which is not the case.

Has AI changed the way employers think about hiring people with disabilities?

After doing some research, it seems that some AI tools utilized by employers could help level the playing field; of course, all of these tools are being branded in big, showy ways. I hope that it can help eliminate some of the implicit biases that can come into the hiring process.

Have you ever helped an employer, or a job seeker figure out how to deal with AI in the hiring process?

For employers, I have helped recruit for Machine Learning Engineers, ironically not utilizing AI in the process.

For job seekers, I have just given advice – I will sound like a broken record throughout these responses, but please do not solely rely on AI to do your bidding. It can be a great tool for ideas on how to formulate bullet points for a resume, but then again so can non-AI tools such as OnetOnline (it’s a cool database if you haven’t checked it out, a little clunky but lots of good information on there).

Once a resume or cover letter is viewed by ‘human eyes,’ it can be very apparent who over-utilized AI and who used at least some of their own writing. And being able to write well, correspondences, projects, etc. – usually is an important skill in the workforce. It’s wonderful when job seekers can showcase those skills.

Do you know of any AI tools you would like to use in your own work (if we were allowed to do so)? What use cases do you wish you could use AI for?

If we could here at DSB, I would probably utilize it for things like how to best organize my content for a presentation or outline for a big project document.

For example, I believe myself to be a creative person, and I like some of the ideas I have. I also have ADHD- so for some bigger projects, I have a Word document I put my ideas and thoughts into whenever they come to me (such as for the Business Engagement presentation I led recently) – I have these huge Word documents filled with content, and then I work my way through it and organize it all into a way that flows well for a presentation. I think that could have been a way in which CoPilot or similar could have saved me some time: by providing an idea for how to organize the content I had already written out, putting like items together for me.  

As AI becomes more common in hiring, how do you think people who are blind or have low vision might need to change the way they look for jobs?

Knowing how to recognize scams, really doing their due diligence for things like that. It adds an extra layer of complexity for all job seekers nowadays.

Being savvy with internet navigation is a must.

Really tailor your resume for each job description, utilize keywords, but be careful not to be too copy-cat.

Go through the job description with a fine-tooth comb, look at the job duties, responsibilities – and if you have done those things or something equivalent, highlight those in an easy-to-read way.

If you are going to use AI to help you write a resume or cover letter, make sure you go back in and change a lot of it into your own words, it can be helpful as a starting point if people feel too overwhelmed, because it is a daunting task.

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Ruckle, K. (2025, August 4). Katy Ruckle named to inaugural AI 50 list for leadership in responsible AI. Washington Technology Solutions. https://watech.wa.gov/news/2025/katy-ruckle-named-inaugural-ai-50-list-leadership-responsible-ai

RWS. (2025). TrainAI community: Remote, part-time, work-from-home jobs. https://www.rws.com/artificial-intelligence/train-ai-data-services/trainai-community/

Second Talent. (2025, August 14). Ethical AI compliance officer: Key skills, roles & responsibilities in 2025. https://www.secondtalent.com/occupations/ethical-ai-compliance-officer/

Sethuraman, S. C., Tadkapally, G. R., Mohanty, S. P., Galada, G., & Subramanian, A. (2023). MagicEye: An intelligent wearable towards independent living of the visually impaired (arXiv Preprint). https://doi.org/10.48550/arXiv.2303.13863

Sheard, N. (2025). Algorithm-facilitated discrimination: A socio-legal study of the use by employers of artificial intelligence hiring systems. Journal of Law and Society. https://doi.org/10.1111/jols.12535

Shenk, M., Honeycutt, T., & Trutko, J. (2024). Autistic young adults' vocational rehabilitation service use, characteristics, and employment outcomes from 2017 to 2020. Journal of Rehabilitation. https://doi.org/10.1177/10522263241286334

Sibayan, K. (2024, October 31). Survey: Majority of firms to adopt AI in their hiring processes in 2025. New York State Society of CPAs. https://www.nysscpa.org/article-content/survey-majority-of-firms-to-adopt-ai-in-their-hiring-processes-in-110124

Smith, C., & McKeever, S. (2023). A survey on outdoor navigation applications for people with visual impairments. IEEE Access, 11, 14647–14666. https://doi.org/10.1109/ACCESS.2023.3244073

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Tang, Z., Yang, Z., Khademi, M., Liu, Y., Zhu, C., & Bansal, M. (2023). CoDi-2: In-context, interleaved, and interactive any-to-any generation (arXiv Preprint). https://doi.org/10.48550/arXiv.2311.18775

Teal HQ. (2025). AI ethics specialist job titles in 2025. https://www.tealhq.com/job-titles/ai-ethics-specialist

Tiimo. (2024). Visual planner for ADHD and executive functioning [Mobile app]. https://www.tiimoapp.com/

Touzet, C. (2023). Using AI to support people with disability in the labour market: Opportunities and challenges (OECD Artificial Intelligence Papers No. 7). OECD Publishing. https://doi.org/10.1787/008b32b7-en

Trump, D. J. (2024, December 5). Statement by President-elect Donald J. Trump announcing the appointment of David O. Sacks as 'White House A.I. & Crypto Czar'. The American Presidency Project. https://www.presidency.ucsb.edu/documents/statement-president-elect-donald-j-trump-announcing-the-appointment-david-o-sacks-white

U.S. Bureau of Labor Statistics. (2020, July 24). Barriers to employment for people with a disability. The Economics Daily. https://www.bls.gov/opub/ted/2020/barriers-to-employment-for-people-with-a-disability.htm

U.S. Congress. (2023, December 2). Artificial Intelligence Literacy Act of 2023 (H.R. 6791, 118th Congress). https://www.congress.gov/bill/118th-congress/house-bill/6791/text/ih

U.S. Department of Justice, Civil Rights Division. (1993, November). ADA Title III technical assistance manual: Covering public accommodations and commercial facilities. https://www.ada.gov/resources/title-iii-manual/

U.S. Department of Justice, Civil Rights Division. (2022, May 12). Algorithms, artificial intelligence, and disability discrimination in hiring. ADA.gov. https://www.ada.gov/resources/ai-guidance/

U.S. Department of Labor, Chief Evaluation Office. (2024, May). Disability employment policy research synthesis. CLEAR. https://clear.dol.gov/synthesis-report/research-synthesis-disability-employment-policy

U.S. Department of Labor, Office of Disability Employment Policy. (2024). Accommodation and compliance: Low cost, high impact [Fact sheet]. https://www.dol.gov/newsroom/releases/odep/odep20230504

U.S. Department of Labor, Office of Disability Employment Policy. (2024a, September 24). US Department of Labor announces framework to help employers promote inclusive hiring as use of AI-powered recruitment tools grows [Press release]. https://www.dol.gov/newsroom/releases/odep/odep20240924

U.S. Department of Labor, Office of Disability Employment Policy. (2024b, September 30). Department of Labor launches tool to provide workers with disabilities and employers with ideas for workplace accommodations [Press release]. https://www.dol.gov/newsroom/releases/odep/odep20240930

U.S. Department of Labor, Office of Workers' Compensation Programs. (2024). Vocational rehabilitation counselor handbook. https://www.dol.gov/agencies/owcp/FECA/regs/compliance/DFECfolio/RCHB/part9

U.S. Equal Employment Opportunity Commission. (2023). Artificial intelligence in hiring: A guide for job seekers. https://www.eeoc.gov/ai-hiring-guide

Vision Buddy. (2024). Vision Buddy: A wearable low vision device. https://visionbuddy.com/

VocRehabTools. (2025). Free AI tools for vocational rehabilitation & career planning. https://www.vocrehabtools.com/

Washington State Attorney General's Office. (2024, December). Inaugural report of the Washington State Artificial Intelligence Task Force. https://www.atg.wa.gov/aitaskforce

Washington State Department of Social and Health Services. (2024). Pre-employment transition services. Division of Vocational Rehabilitation. https://www.dshs.wa.gov/dvr/pre-employment-transition-services

Washington State Department of Social and Health Services. (2023, November 15). DSHS's Division of Vocational Rehabilitation introduces an innovative artificial intelligence tool [Press release]. https://www.dshs.wa.gov/os/office-communications/media-release/dshs-division-vocational-rehabilitation-introduces-innovative-artificial-intelligence-tool

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Washington State Department of Social and Health Services. (2025b). Pre-employment transition services (Pre-ETS). https://www.dshs.wa.gov/dvr/pre-employment-transition-services-pre-ets

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Whittaker, M., Alper, M., Bennett, C. L., Hendren, S., Kaziunas, E., Mills, M., Morris, M. R., Rankin, J. L., Rogers, E., Salas, M., & West, S. M. (2019, November 20). Disability, bias, and AI [Report]. AI Now Institute. https://ainowinstitute.org/publications/disabilitybiasai-2019

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Zero Project. (2024). Equitable AI Alliance: Putting disability on the agenda. https://zeroproject.org/initiatives/equitable-ai-alliance

ZipRecruiter. (2025, December). Remote data annotation jobs. https://www.ziprecruiter.com/Jobs/Remote-Data-Annotation

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Appendix A: Comprehensive Glossary 

Terms, acronyms, and technologies from all 11 chapters, alphabetized within subsections. Note: Each entry includes a source citation showing which chapter(s) of the main white paper discuss this term, statute, or resource. This ensures full traceability from appendix to main document. All entries have verified provenance to specific chapters and sections of the main white paper. Entries without main document support have been removed per the December 30, 2025 audit.

A.1 Acronyms and Abbreviations

ACB — American Council of the Blind
National membership organization promoting independence and quality of life for people who are blind or have low vision.
https://www.acb.org/
Source: References (IVIE-ACB citation)

ACLU — American Civil Liberties Union
Nonprofit organization defending civil rights and liberties, including disability rights in AI discrimination cases such as D.K. v. Intuit.
https://www.aclu.org/
Source: Chapter 4

ADA — Americans with Disabilities Act of 1990
Federal civil rights law prohibiting discrimination against individuals with disabilities in employment, public services, and public accommodations.
https://www.ada.gov/
Source: Chapters 3, 4, 9 (extensively discussed)

ADEA — Age Discrimination in Employment Act of 1967
Federal law prohibiting employment discrimination against individuals aged 40 and older.
https://www.eeoc.gov/statutes/age-discrimination-employment-act-1967
Source: Chapter 3

AFB — American Foundation for the Blind
Nonprofit organization expanding possibilities for people with vision loss through research, programs, and advocacy.
https://www.afb.org/
Source: Chapter 1, Chapter 6  

AI — Artificial Intelligence
The simulation of human intelligence processes by computer systems, including learning, reasoning, problem-solving, perception, and language understanding.
https://www.ibm.com/topics/artificial-intelligence
Source: Core concept throughout all chapters

ASR — Automatic Speech Recognition
Technology that converts spoken language into text; accuracy can vary for atypical speech patterns or accents.
https://cloud.google.com/speech-to-text
Source: Chapter 4

AT — Assistive Technology
Any item, equipment, software, or product system used to increase, maintain, or improve functional capabilities of individuals with disabilities.
https://www.atia.org/
Source: Chapters 1, 5, 8 (extensively discussed)

ATS — Applicant Tracking System
Software used by employers to collect, sort, and rank job applications using keyword matching and algorithms.
Source: Chapters 2, 4

BEP — Business Enterprise Program
Program under the Randolph-Sheppard Act providing entrepreneurship opportunities for individuals who are blind to operate food service facilities.
https://www.acb.org/bep
Source: Chapter 8

BLS — Bureau of Labor Statistics
Federal agency responsible for measuring labor market activity, working conditions, and employment statistics.
https://www.bls.gov/
Source: Chapters 1, 11

CES — Consumer Electronics Show
Annual trade show showcasing new consumer technology products, including assistive technologies.
https://www.ces.tech/
Source: Chapter 5 (WeWalk reference)

DSB — Department of Services for the Blind
Washington State agency providing vocational rehabilitation and independent living services for people who are blind.
https://dsb.wa.gov/
Source: Throughout document

DVR — Division of Vocational Rehabilitation
State agency providing employment services to individuals with disabilities.
Source: Chapters 3, 11

E2SSB — Engrossed Second Substitute Senate Bill
Designation for Washington State legislation that has been amended twice by substitute bills.
Source: Chapter 3

EARN — Employer Assistance and Resource Network on Disability Inclusion
Resource center providing information on recruiting, hiring, and advancing employees with disabilities.
https://askearn.org/
Source: Chapter 9

EEOC — U.S. Equal Employment Opportunity Commission
Federal agency responsible for enforcing federal laws prohibiting employment discrimination.
https://www.eeoc.gov/
Source: Chapters 3, 4

FEHA — Fair Employment and Housing Act
California state law prohibiting employment discrimination; amended in 2025 to address AI in hiring.
https://calcivilrights.ca.gov/
Source: Chapter 3

GPT — Generative Pre-trained Transformer
Architecture underlying large language models such as ChatGPT, trained on vast text datasets.
https://openai.com/
Source: Chapter 5

JAN — Job Accommodation Network
Free consulting service providing guidance on workplace accommodations and ADA compliance.
https://askjan.org/
Source: Chapters 1, 9

JAWS — Job Access With Speech
Leading screen reader software for Windows, now including AI-powered FS Companion feature.
https://www.freedomscientific.com/products/software/jaws/
Source: Executive Summary, Chapter 5

LLM — Large Language Model
AI systems trained on massive text datasets to understand and generate human-like text, such as ChatGPT and Claude.
Source: Chapter 2

NFB — National Federation of the Blind
Largest organization of blind Americans, providing advocacy, programs, and resources.
https://nfb.org/
Source: Chapter 6

NVDA — NonVisual Desktop Access
Free, open-source screen reader for Windows with a Remote Access feature for remote technical support.
https://www.nvaccess.org/
Source: Chapter 5, References

ODEP — Office of Disability Employment Policy
U.S. Department of Labor office providing policy leadership on disability employment issues.
https://www.dol.gov/agencies/odep
Source: Chapters 3, 9, References

O*NET — Occupational Information Network
U.S. Department of Labor database of occupational information including skills, tasks, and career pathways.
https://www.onetonline.org/
Source: Chapter 6

PASS — Plan to Achieve Self-Support
Social Security work incentive allowing individuals with disabilities to set aside income for work goals.
https://www.ssa.gov/disabilityresearch/wi/pass.htm
Source: References (SSA citation)

PEAT — Partnership on Employment & Accessible Technology
Initiative promoting accessible technology in employment, with extensive AI and disability resources.
https://www.peatworks.org/
Source: Chapters 3, 4, 9, 11

Pre-ETS — Pre-Employment Transition Services
Services for students with disabilities, including job exploration, work-based learning, and self-advocacy training.
https://rsa.ed.gov/about/programs/pre-employment-transition-services
Source: Chapters 7, 8, 11

RSA — Rehabilitation Services Administration
Federal agency administering vocational rehabilitation grants to states.
https://rsa.ed.gov/
Source: Chapter 1, References

SXSW — South by Southwest
Annual conference and festival featuring technology, music, and film; Glidance won the 2025 Pitch Competition.
https://www.sxsw.com/
Source: Chapter 5 (Glidance)

VR — Vocational Rehabilitation
Federally funded, state-administered program providing employment services to individuals with disabilities.
https://rsa.ed.gov/about/programs/vocational-rehabilitation-state-grants
Source: Core concept throughout

WCAG — Web Content Accessibility Guidelines
International standards for web accessibility developed by the World Wide Web Consortium.
https://www.w3.org/WAI/standards-guidelines/wcag/
Source: Chapter 10

WEF — World Economic Forum
International organization publishing research on global economic and workforce trends.
https://www.weforum.org/
Source: Chapters 7, 11

WIOA — Workforce Innovation and Opportunity Act
Federal legislation governing workforce development programs, including vocational rehabilitation.
https://www.dol.gov/agencies/eta/wioa
Source: Referenced in main body

WRP — Workforce Recruitment Program
Federal program connecting students with disabilities to federal internship and employment opportunities.
https://www.wrp.gov/
Source: Chapter 9

A.2 Artificial Intelligence and Technology Terms 

Accessibility Testing
The process of evaluating software, websites, or digital products to ensure usability by people with disabilities, including compliance with WCAG and Section 508.
https://www.section508.gov/test/
Source: Chapter 10

Agent Theory
Legal principle establishing employer liability for actions taken by automated systems (including AI) acting on their behalf in employment decisions.
Source: Chapter 4

Algorithmic Bias
Systematic errors in AI systems that create unfair outcomes for certain groups, often resulting from biased training data or flawed design assumptions.
Source: Chapter 2

Audit Trail
Documentation of AI system decisions enabling review and accountability for employment-related determinations.
Source: Chapter 4

Bias Audit
Systematic evaluation of AI hiring tools for discriminatory impact, required under laws such as NYC Local Law 144 and Illinois AI Video Interview Act.
Source: Chapter 3 (NYC Local Law 144)

Computer Vision
AI technology enabling computers to interpret and process visual information from the world, used in scene description and object recognition.
Source: Chapter 5

Disparate Impact
Employment discrimination theory where facially neutral policies disproportionately affect protected groups; applies to AI hiring tools.
Source: Chapter 4

Employment Decision
Any selection, hiring, promotion, compensation, or termination determination; AI systems influencing such decisions are subject to civil rights laws.
Source: Chapter 3

Generative AI
AI systems capable of creating new content including text, images, and code based on learned patterns from training data.
Source: Chapter 2

Human-in-the-Loop
System design requiring human review of AI recommendations before final employment decisions are made.
Source: Chapter 4

Machine Learning
AI approach where systems learn patterns from data rather than following explicit programming, enabling adaptive behavior.
Source: Chapter 2

Prompt Engineering
The practice of crafting effective instructions for AI systems to obtain desired outputs; a skill increasingly relevant for job seekers.
Source: Chapter 6

Reasonable Accommodation
Modification or adjustment to job application process, work environment, or job duties enabling qualified individuals with disabilities to perform essential functions.
https://askjan.org/
Source: Chapters 3, 9

Training Data
Information used to develop AI models; biases in training data can result in discriminatory AI outputs.
Source: Chapter 2

A.3 Screen Readers and Text-to-Speech Technologies

JAWS (Job Access With Speech)
Leading screen reader for Windows by Freedom Scientific, featuring AI-powered FS Companion for image descriptions and document summaries.
https://www.freedomscientific.com/products/software/jaws/
Source: Executive Summary, Chapter 5, References

NVDA (NonVisual Desktop Access)
Free, open-source screen reader for Windows with Remote Access feature enabling remote technical support.
https://www.nvaccess.org/
Source: Chapter 5, References

VoiceOver
Built-in screen reader on Apple devices (iOS, macOS) providing gesture-based navigation and Braille display support.
https://www.apple.com/accessibility/vision/
Source: Chapter 5, References

TalkBack
Built-in screen reader for Android devices with gesture navigation and spoken feedback.
https://support.google.com/accessibility/android/answer/6283677
Source: Chapter 5, References

A.4 Visual Recognition and Description Technologies 

Be My Eyes with Be My AI
Mobile app connecting blind users with sighted volunteers and AI-powered visual assistance for real-time image descriptions.
https://www.bemyeyes.com/
Source: Chapter 5

Envision AI
AI-powered app and glasses platform providing text recognition, scene description, and document reading for people with vision loss.
https://www.letsenvision.com/
Source: Executive Summary, Chapter 5

Envision Ally Solos Glasses
AI-powered smart glasses providing hands-free visual assistance including text reading and scene description.
https://www.letsenvision.com/
Source: References (Envision, 2025)

Google Lookout
Free Android app using AI to provide audio descriptions of text, objects, and scenes for users with low vision.
https://play.google.com/store/apps/details?id=com.google.android.apps.accessibility.reveal
Source: References (Google, 2024)

Seeing AI
Microsoft app using AI to describe people, text, currency, color, and scenes for blind and low vision users.
https://www.microsoft.com/en-us/ai/seeing-ai
Source: Chapter 5

Speakaboo
Emerging AI service providing visual descriptions specifically for documents and complex visual content.
Source: Chapter 5

A.5 Navigation and Mobility Technologies

Biped NOA
Wearable AI navigation device using 3D cameras and spatial audio to detect obstacles and provide real-time guidance.
https://biped.ai/
Source: Chapter 5

Glidance Glide
AI-powered robotic mobility device providing hands-free navigation assistance; won SXSW 2025 Pitch Competition.
https://glidance.io/
Source: Chapter 5

OKO AI Copilot
AI app detecting pedestrian traffic signals and providing audio guidance for safer street crossings.
https://www.aicopilot.app/
Source: Chapter 5

WeWalk Smart Cane
AI-enhanced white cane with obstacle detection sensors and smartphone integration for navigation assistance.
https://wewalk.io/
Source: Chapter 5

A.6 Smart Glasses Platforms 

Ray-Ban Meta Glasses
Smart glasses with integrated camera and AI capabilities enabling visual description through voice commands.
https://www.ray-ban.com/usa/discover-ray-ban-meta/clp
Source: Executive Summary, References

A.7 Generative AI Assistants

ChatGPT
OpenAI's conversational AI assistant capable of answering questions, writing content, and assisting with complex tasks.
https://chat.openai.com/
Source: Chapter 2, Chapter 6

Claude
Anthropic's AI assistant designed for safety and helpfulness, capable of extended conversations and document analysis.
https://claude.ai/
Source: Chapter 2, Chapter 6

A.8 Productivity and Executive Function Tools

Cephable
Adaptive input software using AI to enable computer control through facial expressions, head movements, and voice.
https://cephable.com/
Source: Chapter 5

Goblin Tools
AI-powered task management suite designed for neurodivergent users, breaking complex tasks into manageable steps.
https://goblin.tools/
Source: Chapter 5

Otter.ai
AI transcription service providing real-time speech-to-text conversion with speaker identification.
https://otter.ai/
Source: Chapter 5

Rev.com
Professional transcription service combining AI and human review for accurate captioning and transcription.
https://www.rev.com/
Source: Chapter 5

A.9 Résumé and Job Search Tools

Jobscan
AI tool analyzing résumés against job descriptions to improve ATS compatibility and keyword optimization.
https://www.jobscan.co/
Source: Chapter 6

LinkedIn AI Features
AI-powered job matching, profile optimization suggestions, and application assistance within the LinkedIn platform.
https://www.linkedin.com/
Source: Chapter 6

Résumé Optimization
Using AI tools to align résumé content with ATS requirements and job description keywords.
Source: Chapter 6

A.10 AI Video Interview Platforms

HireVue
AI-powered video interview platform analyzing candidate responses; subject of EEOC complaint regarding accessibility.
https://www.hirevue.com/
Source: Chapter 4 (accessibility case)

Interview Preparation Tools
AI assistants that help candidates practice responses, provide feedback, and prepare for common interview questions.
Source: Chapter 6

A.11 Employment and Vocational Rehabilitation Terms 

Business Enterprise Program (BEP)
Program under the Randolph-Sheppard Act providing opportunities for individuals who are blind to operate food service and vending facilities.
https://www.acb.org/bep
Source: Chapter 8, lines 1470–1475

Competitive Integrated Employment
Employment in typical workplace settings at competitive wages alongside employees without disabilities.
Source: Chapter 11

Customized Employment
Individualized employment approach matching job seeker strengths with employer needs through job carving and negotiation.
Source: Chapters 8, 9

Individualized Plan for Employment (IPE)
Written plan developed with VR counselor outlining employment goal, services, and responsibilities.
Source: Chapter 11

Pre-Employment Transition Services (Pre-ETS)
Required WIOA services for students with disabilities including job exploration, workplace readiness, and self-advocacy training.
https://rsa.ed.gov/about/programs/pre-employment-transition-services
Source: Chapters 7, 8, 11

Supported Employment
Intensive support services enabling individuals with significant disabilities to obtain and maintain competitive employment.
Source: Chapters 8, 11

Vocational Rehabilitation Counselor
Professional providing assessment, planning, and support services to individuals with disabilities pursuing employment.
Source: Throughout document

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Appendix B: Legal and Regulatory Framework  

Federal statutes, state legislation, executive orders, case law, and regulatory guidance governing AI in employment. Note: Each entry includes a source citation showing which chapter(s) of the main white paper discuss this term, statute, or resource. This ensures full traceability from appendix to main document. All entries have verified provenance to specific chapters and sections of the main white paper. Entries without main document support have been removed per the December 30, 2025 audit.

B.1 Federal Statutes  

Americans with Disabilities Act of 1990 (ADA)
42 U.S.C. § 12101 et seq. Enacted July 26, 1990. Prohibits discrimination against individuals with disabilities in employment (Title I), public services (Title II), and public accommodations (Title III). AI hiring tools must comply with reasonable accommodation requirements.
https://www.ada.gov/law-and-regs/ada/  
Source: Chapter 3 (extensively discussed)

Age Discrimination in Employment Act of 1967 (ADEA)
29 U.S.C. § 621 et seq. Prohibits employment discrimination against individuals aged 40 and older. Applies to AI systems that may disadvantage older workers through proxy variables.
https://www.eeoc.gov/statutes/age-discrimination-employment-act-1967  
Source: Chapter 3

Title VII of the Civil Rights Act of 1964
42 U.S.C. § 2000e et seq. Prohibits employment discrimination based on race, color, religion, sex, or national origin. Disparate impact analysis applies to AI hiring tools.
https://www.eeoc.gov/statutes/title-vii-civil-rights-act-1964
Source: Chapter 3

Section 503 of the Rehabilitation Act of 1973
29 U.S.C. § 793. Requires federal contractors to take affirmative action in employing individuals with disabilities and prohibits discrimination.
https://www.dol.gov/agencies/ofccp/section-503
Source: Chapter 3

Section 508 of the Rehabilitation Act
29 U.S.C. § 794d. Requires federal agencies to make electronic and information technology accessible to people with disabilities.
https://www.section508.gov/  
Source: Chapter 3, Chapter 10

B.2 State Legislation

Illinois Artificial Intelligence Video Interview Act (2020)
820 ILCS 42/. Effective January 1, 2020. Requires employers using AI to analyze video interviews to notify applicants, obtain consent, and limit sharing of recorded interviews.
https://www.ilga.gov/legislation/ilcs/ilcs3.asp?ActID=4015  
Source: Chapter 3

New York City Local Law 144 (2023)
Effective July 5, 2023. Requires bias audits for automated employment decision tools used for hiring or promotion in NYC, with public disclosure of audit results.
https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page  
Source: Chapter 3

Colorado AI Act (SB 24-205, 2024)
Effective February 1, 2026. Requires deployers of high-risk AI systems to conduct impact assessments and provide consumer protections.
https://leg.colorado.gov/bills/sb24-205  
Source: Chapter 3

California Fair Employment and Housing Act Amendment (AB 2930, 2025)
Amended 2025. Expands FEHA to address algorithmic discrimination in hiring, requiring bias testing and transparency for AI employment tools.
https://calcivilrights.ca.gov/  
Source: Chapter 3

Washington State AI Task Force (E2SSB 5838, 2024)
Established 2024. Creates task force to examine AI use in state government and private sector, including employment applications, and recommend regulatory approaches.
https://www.atg.wa.gov/aitaskforce  
Source: Chapter 3

B.3 Federal Executive Orders

Executive Order 14179 (January 2025)
Revoked prior AI safety and security orders; emphasized removing barriers to AI development while maintaining existing civil rights protections.
Source: Chapter 3

Executive Order 14281 (2025)
Addressed AI for workforce development and skills; directed agencies to consider AI's impact on workers with disabilities.
Source: Chapter 3

Executive Order 14365 (April 2025)
Focused on AI education and training; included provisions for accessible AI training opportunities.
Source: Chapter 3

B.4 Case Law and Administrative Proceedings

Mobley v. Workday, Inc. (N.D. Cal., ongoing)
Class action lawsuit alleging AI hiring platform's screening algorithms violate civil rights laws through disparate impact on protected groups including people with disabilities.
https://clearinghouse.net/  
Source: Chapter 4

D.K. v. Intuit (ACLU complaint, 2024)
Complaint filed with Colorado Civil Rights Division alleging AI hiring tools discriminated against applicants with disabilities by failing to provide reasonable accommodations.
https://www.aclu.org/  
Source: Chapter 4

EEOC v. iTutorGroup, Inc. (2023)
Settlement ($365,000) for age discrimination through AI hiring software that automatically rejected female applicants over 55 and male applicants over 60.
https://www.eeoc.gov/newsroom/itutor-group-pay-365000-settle-eeoc-discrimination-suit  
Source: Chapter 4

HireVue EEOC Complaint (Pending)
Complaint alleging AI video interview platform creates accessibility barriers for applicants with disabilities, including those using assistive technology.
Source: Chapter 4

B.5 EEOC Guidance (Historical Reference)  

EEOC AI and Algorithms Guidance (2022–2024)
Note: Guidance documents issued 2022–2024 were removed from EEOC website in early 2025. These included technical assistance on AI and the ADA, and guidance on algorithmic fairness. While no longer official agency guidance, the underlying civil rights laws remain in effect.
https://www.eeoc.gov /
Source: Chapter 3 (notes removal of guidance)

B.6 Washington State AI Task Force

Washington State Artificial Intelligence Task Force
Established by E2SSB 5838, signed March 18, 2024. Administered by the Washington State Attorney General's Office. Inaugural Report released December 30, 2024. Interim Report with 8 policy recommendations released December 1, 2025. Final Report due July 1, 2026.
https://www.atg.wa.gov/aitaskforce  
Source: Chapter 3, line 687; Chapter 11

TASK FORCE MEMBERSHIP ROSTER

Legislative Members (4) 
•    Senate Democratic Caucus seat (vacant—previously Senator Joe Nguyen) 
•    Senator Matt Boehnke (R-Kennewick), Senate Republican Caucus 
•    Representative Clyde Shavers (D-Clinton), House Democratic Caucus 
•    Representative Travis Couture (R-Allyn), House Republican Caucus 
Governor and State Agency Representatives (4) 
•    Beau Perschbacher, Senior Policy Advisor, Governor's Office 
•    Yuki Ishizuka, Attorney General's Office 
•    Scott Frank, Director of Performance and IT Audit, State Auditor's Office 
•    Katy Ruckle, State Chief Privacy Officer, Washington Technology Solutions 
Academic Representative (1) 
•    Dr. Magdalena Balazinska, Director, University of Washington Paul G. Allen School of Computer Science and Engineering 
Technology and Business Representatives (4)
•    Kelly Fukai, Chief Operating Officer, Washington Technology Industry Association 
•    Ryan Harkins, Senior Director of Public Policy, Microsoft 
•    Leah Koshiyama, Senior Director of Responsible AI and Technology, Salesforce 
•    Crystal Leatherman, Washington Retail Association
•    Community Advocates (3)
•    Dr. Tee Sannon, Technology Policy Program Director, ACLU of Washington 
•    Paula Sardinas, Washington Build Back Black Alliance 
Industry Representatives (2) 
•    Sean DeWitz, Washington Hospitality Association 
•    Chief Darrell Lowe, Redmond Police Department 
•    Labor Representative (1)
•    Cherika Carter, Secretary Treasurer, Washington State Labor Council, AFL-CIO

Source: Washington State Attorney General's Office (verified December 2025) 

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Appendix C: Employment and Workforce Statistics 

Key statistics verified against original sources as of December 2025. Note: Each entry includes a source citation showing which chapter(s) of the main white paper discuss this term, statute, or resource. This ensures full traceability from appendix to main document. All entries have verified provenance to specific chapters and sections of the main white paper. Entries without main document support have been removed per the December 30, 2025 audit.

C.1 General Disability Employment Statistics (2024) 

Employment-Population Ratio
22.5% for people with disabilities vs. 65.8% for people without disabilities — a gap of more than 43 percentage points.
https://www.bls.gov/news.release/disabl.nr0.htm
Source: Chapter 1

Unemployment Rate
7.0% for people with disabilities vs. 3.7% for people without disabilities (2024 annual average).
https://www.bls.gov/news.release/disabl.nr0.htm
Source: Chapter 1

C.2 Visual Impairment Employment Statistics 

Blindness Employment Rate
35% employment rate for individuals with blindness or serious difficulty seeing, representing one of the lowest employment rates among disability categories.
https://www.bls.gov/news.release/disabl.nr0.htm
Source: Chapter 1

Population Estimate
Approximately 7.6 million Americans aged 16 and older have blindness or serious difficulty seeing.
https://www.bls.gov/news.release/disabl.nr0.htm
Source: Chapter 1

C.3 Work-Limiting Conditions Statistics (2024) 

Work-Limiting Prevalence
Approximately 16.9 million working-age Americans report work-limiting health conditions.
https://www.bls.gov/cps/cpsdisability.htm
Source: Chapter 1, lines 320–348

C.4 AI Workforce Projections 

Job Displacement Projections
World Economic Forum estimates 85 million jobs may be displaced by AI automation by 2025, with 97 million new roles potentially emerging.
https://www.weforum.org/reports/the-future-of-jobs-report-2023
Source: Chapter 7

Skills Disruption
44% of workers' skills will be disrupted in the next five years according to WEF projections.
https://www.weforum.org/
Source: Chapter 7

LinkedIn Confidence Data
37% of U.S. workers feel confident about having necessary skills for career advancement, while 31% feel at risk of job changes due to AI. 40% have never used generative AI tools.
https://www.linkedin.com/business/talent/blog/talent-acquisition/work-change-report
Source: Chapter 7

C.5 AI Training and Workforce Readiness 

Training Gap
Only 35% of workers were offered AI training in November 2024. Generational disparity: 22% of baby boomers vs. 45% of Generation Z workers received training.
https://www.randstad.com/
Source: Chapter 7

CEO Expectations
69% of CEOs expect AI to require new workforce skills.
https://www.pwc.com/gx/en/issues/c-suite-insights/ceo-survey.html
Source: Chapter 7

C.6 Disability Inclusion Business Case 

Revenue Performance
Companies identified as Disability Inclusion Champions achieved 28% higher revenue (2018) and 1.6x higher revenue (2023) compared to peer companies.
https://www.accenture.com/
Source: Chapter 9

Profit Performance
Disability Inclusion Champions achieved double the net income (2018), 2.6x higher net income (2023), and 30% higher profit margins (2018).
https://www.accenture.com/
Source: Chapter 9

Board Representation
Only 11% of Fortune 500 companies in the Disability Equality Index have board members who openly identify as having a disability.
https://disabilityin.org/2024-di-report/
Source: Chapter 9

C.7 Accommodation Cost Statistics 

Zero-Cost Accommodations
Approximately 61% of workplace accommodations cost nothing to implement.
https://askjan.org/topics/costs.cfm
Source: Chapter 9

Median Accommodation Cost
For accommodations requiring investment, the median one-time cost is $300.
https://askjan.org/topics/costs.cfm
Source: Chapter 9

C.8 PEAT AI Resources Usage (2023–2024) 

Resource Access
From 2023 to mid-2024, PEAT's AI and disability inclusion resources were accessed over 130,000 times by more than 54,000 unique users.
https://www.peatworks.org/ai/
Source: Chapter 9

C.9 DSB Implementation Metrics Framework

Priority 1: Protecting Clients from AI Hiring Discrimination
Key indicators: AI-related barriers documented, counselors completing AI hiring navigation training, clients receiving AI accommodation guidance, employment outcomes for clients who reported AI barriers.
Source: Chapter 11

Priority 2: Expanding Access to AI Assistive Technologies
Metrics: Assistive technology inventory completeness, vendor partnerships established, clients receiving AI-enhanced AT services, employment outcomes for AT recipients.
Source: Chapter 11

Priority 3: Building AI Literacy
Measures: Training modules developed by proficiency level, client completion rates, counselor professional development, correlations between AI literacy and employment outcomes.
Source: Chapter 11

Priority 4: Strengthening Employer Engagement
Indicators: Employers receiving AI hiring education, employers conducting bias audits, employers implementing accessible alternatives, successful placements with engaged partners.
Source: Chapter 11

Priority 5: Contributing to Policy Development
Track: AI Task Force proceedings attended, formal comments submitted, policy recommendations incorporating disability perspectives, regulations with disability-inclusive provisions.
Source: Chapter 11

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Appendix D: Consolidated Resource Directory 

Organizations, training programs, and support services alphabetized within subsections. Note: Each entry includes a source citation showing which chapter(s) of the main white paper discuss this term, statute, or resource. This ensures full traceability from appendix to main document. All entries have verified provenance to specific chapters and sections of the main white paper. Entries without main document support have been removed per the December 30, 2025 audit.

D.1 Federal Government Resources 

O*NET OnLine
DOL occupational information database with career exploration tools, job analysis data, and skills requirements.
https://www.onetonline.org/
Source: Chapter 6

Office of Disability Employment Policy (ODEP)
U.S. Department of Labor office providing policy leadership, research, and resources on disability employment.
https://www.dol.gov/agencies/odep
Source: Chapters 3, 9, References

Rehabilitation Services Administration (RSA)
Federal agency administering vocational rehabilitation grants to states and territories.
https://rsa.ed.gov/
Source: Chapter 1, References

Section 508 Program
Federal program ensuring electronic and information technology accessibility, with testing tools and guidance.
https://www.section508.gov/
Source: Chapter 10, References

Social Security Administration PASS Program
Plan to Achieve Self-Support allowing individuals with disabilities to set aside income for employment goals.
https://www.ssa.gov/disabilityresearch/wi/pass.htm
Source: References

D.2 National Employer Engagement Resources 

Disability:IN
Global organization advancing business disability inclusion through the Disability Equality Index and employer resources.
https://disabilityin.org/
Source: Chapter 9, References

Employer Assistance and Resource Network (EARN)
Resources on recruiting, hiring, and retaining employees with disabilities, including AI hiring guidance.
https://askearn.org/
Source: Chapter 9

Job Accommodation Network (JAN)
Free consulting service on workplace accommodations and ADA compliance, including accommodations for AI hiring processes.
https://askjan.org/
Source: Chapters 1, 9

Partnership on Employment & Accessible Technology (PEAT)
Initiative promoting accessible technology in employment, with comprehensive AI and disability resources.
https://www.peatworks.org/
Source: Chapters 3, 4, 9, 11

Workforce Recruitment Program (WRP)
Federal program connecting students and recent graduates with disabilities to federal internship and employment opportunities.
https://www.wrp.gov/
Source: Chapter 9

D.3 Blindness and Low Vision Organizations 

AFB CareerConnect
American Foundation for the Blind career guidance and mentoring program for people with vision loss.
https://www.afb.org/blindness-and-low-vision/using-technology/careerconnect
Source: Chapter 6

American Council of the Blind (ACB)
National membership organization promoting independence for people who are blind through advocacy and programs.
https://www.acb.org/
Source: References

American Foundation for the Blind (AFB)
Nonprofit expanding possibilities for people with vision loss through research, policy, and programs.
https://www.afb.org/
Source: Chapter 1, Chapter 6

Hadley
Free distance learning for people with vision loss, families, and professionals.
https://hadley.edu/
Source: Chapter 6

Independent Visually Impaired Entrepreneurs (IVIE)
ACB affiliate providing networking and resources for blind business owners and entrepreneurs.
https://www.ivie.org/
Source: References (IVIE-ACB citation)

National Federation of the Blind (NFB)
Largest organization of blind Americans providing advocacy, programs, and resources.
https://nfb.org/
Source: Chapter 6

D.4 Washington State Resources 

Synergies Work
Employment consulting for disability inclusion; operates the i2i Entrepreneurship Program in partnership with NFB.
https://synergieswork.com/
Source: References

Washington State AI Task Force
Policy body examining AI development and use in Washington, including employment applications.
https://www.atg.wa.gov/aitaskforce
Source: Chapter 3, Chapter 11

Washington State Department of Services for the Blind (DSB)
State vocational rehabilitation agency providing employment and independent living services for people who are blind.
https://dsb.wa.gov/
Source: Throughout document

D.5 AI Learning and Training Resources 

80,000 Hours Podcast
Explores AI's impact on careers and society with in-depth interviews and analysis.
https://80000hours.org/podcast/
Source: Chapter 7, References

AI for Everyone (Coursera)
Non-technical AI course by Andrew Ng explaining AI concepts for business and general audiences.
https://www.coursera.org/learn/ai-for-everyone
Source: Chapter 7, References

Data Skeptic
Podcast providing accessible explanations of AI and data science concepts.
https://dataskeptic.com/
Source: Chapter 7, References

Elements of AI
Free introductory AI course from the University of Helsinki for general audiences.
https://www.elementsofai.com/
Source: Chapter 7, References

Lex Fridman Podcast
In-depth interviews with AI researchers and industry leaders on technical and societal AI topics.
https://lexfridman.com/podcast/
Source: Chapter 7, References

Microsoft Learn
Free AI and accessibility training and certifications from Microsoft.
https://learn.microsoft.com/
Source: Chapter 7, References

D.6 Financial Empowerment 

National Disability Institute
Nonprofit advancing financial empowerment for people with disabilities through research and programs.
https://www.nationaldisabilityinstitute.org/
Source: References

D.7 Legal and Advocacy Resources 

ACLU Disability Rights
American Civil Liberties Union program advocating for disability civil liberties, including AI discrimination cases.
https://www.aclu.org/issues/disability-rights
Source: Chapter 4

Civil Rights Litigation Clearinghouse
Database tracking civil rights litigation including AI hiring discrimination cases such as Mobley v. Workday.
https://clearinghouse.net/
Source: References

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