Equal Access or Algorithmic Barriers?

AI and the Fight for Disability-Inclusive Hiring

Abstract

This paper examines how artificial intelligence (AI) hiring tools, while marketed as objective and free of bias, perpetuate structural discrimination against individuals with disabilities. By tracing the historical legacy of ableism in employment, from early personality testing to modern algorithmic screening, the paper situates AI-driven recruitment and selection within a broader pattern of exclusion. It argues that biases embedded in AI design, misrepresentative training data, and inaccessible application processes reproduce barriers that the Rehabilitation Act and Americans with Disabilities Act sought to eliminate. Through the case of Mobley v. Workday, the paper highlights the legal and ethical challenges of algorithmic discrimination, including diminished transparency, accountability, and informed consent. Ultimately, it proposes a four-part framework for disability-inclusive AI governance: increasing diversity in AI development and training, mandating auditing and impact assessments, enforcing privacy and consent protections, and requiring human oversight in employment decisions. The paper concludes that equitable AI hiring demands proactive policy intervention and renewed enforcement of disability rights principles to ensure true inclusion in the digital labor market.