AI is part of our daily lives, and it is changing employment. Employers use AI to screen resumes, deciding which candidates are worth a closer look. This is supposed to make hiring easier, but it also means some candidates get overlooked.
Thousands of businesses now use Workday. In lawsuit filed recently, the plaintiffs accused Workday of discriminating against disabled people through its machine learning-based recruitment software. This algorithm uses proxies that could misunderstand the history of an applicant. If an applicant is disabled, there could be gaps in their resume, but AI algorithms only see those gaps as a risk.
For many disabled people, the biases go beyond their disability. For instance, a disabled applicant with a particular name could be subjected to discrimination even before the hiring process starts. That is because AI learns from previous information, which can include biases surrounding racism, xenophobia, and religion. Therefore, a disabled person who is also Black, Muslim, Latino or an immigrant might face even more discrimination.
The lawsuit is allowed to proceed. It is not clear evidence of discrimination by Workday. On its own, it illustrates the very fragile nature of fairness when a computerized system is the one looking at the job application.
Right now the job market is rough for everyone, but even more so for disabled people. Research from the Bureau of Labor Statistics shows that 22.8% of disabled people were working compared to 66.6% of non-disabled people in 2025. Disabled people are also more likely to be working on a part-time basis, freelancing, or working remotely.
That’s part of what makes the lawsuit resonate. It shows quickly something that appears to be neutral can become something negative. Consider an AI algorithm that doesn’t understand why someone communicates differently, or why someone’s work history is inconsistent, or why someone has trouble with an online assessment.
A disabled applicant may be able to do a job. They may have the right qualifications. Sadly, their application may be ignored by an algorithm that misunderstands their application.
Federal agencies have warned the public about this issue for years now. In 2022, both the Equal Employment Opportunity Commission and the Department of Justice warned that using AI in hiring could violate the Americans with Disabilities Act, allowing employers to screen out disabled applicants who are otherwise qualified for the work.
When it comes to finding employment, AI is a complicated tool. Disabled people already encounter enough barriers to employment. AI is only creating additional barriers.
Sources:
Bommasani, Rishi, et al. “Algorithmic Monocultures in Hiring.” Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency, 2026, pp. 6351–6382. https://doi.org/10.1145/3805689.3812400.
Booker, Mario DeSean, and FaLessia Camille Booker. “The Name Game: Algorithmic Gatekeeping and the Systematic Exclusion of Ethnic Names in Digital Hiring.” International Journal of Research and Innovation in Social Science, 3 Sept. 2025, pp. 2210–2223. https://rsisinternational.org/journals/ijriss/Digital-Library/volume-9-issue-8/2210-2223.pdf
Katzman, Jared. “Artificial Intelligence (AI) Hiring Technology and Disability Discrimination.” Science, Technology and Public Policy, University of Michigan, 28 Aug. 2024, https://stpp.fordschool.umich.edu/research/community-resource/artificial-intelligence-ai-hiring-technology-and-disability.
Marbella, Jean. “AI Weeds Out Older and Minority Job Applicants, Lawsuit Against Workday Claims.” The Baltimore Sun, 1 Aug. 2026, https://www.baltimoresun.com/2026/08/01/workday-ai-hiring-lawsuit-maryland/.
“People with a Disability: Labor Force Characteristics—2025.” U.S. Bureau of Labor Statistics, 3 Mar. 2026, https://www.bls.gov/news.release/disabl.nr0.htm.
Wiessner, Daniel. “Workday Must Face California Lawsuit over AI Bias in Job Screening Tools.” Reuters, 22 June 2026, https://www.reuters.com/legal/government/workday-must-face-california-lawsuit-over-ai-bias-job-screening-tools-2026-06-22/.
Wilson, Kyra, and Aylin Caliskan. “Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval.” Proceedings of the AAAI ACM Conference on AI, Ethics, and Society, vol. 7, no. 1, 16 Oct. 2024, pp. 1578–1590, https://ojs.aaai.org/index.php/AIES/article/view/31748/33915.