Workday Faces Landmark Lawsuit Over AI Hiring: What HR Leaders Need to Know
A federal court has allowed the central age-discrimination claim in Mobley v. Workday, Inc. to proceed as a collective action. Notices will go out to potential plaintiffs, opening the door for others to opt in.
This isn't a small ripple. Workday powers HR and finance operations across a majority of the Fortune 500 and serves customers in 175 countries. If a court finds vendor liability for AI-driven screening, the impact will reach far beyond one platform.
What the Court Just Decided
The case is in the U.S. District Court for the Northern District of California. Workday argued that employers, not software vendors, make hiring decisions and that its tools organize and rank applicants.
Judge Rita F. Lin allowed the case to move forward, noting Workday's algorithms may materially influence outcomes in ways that require legal scrutiny. The age-discrimination claim under the ADEA can proceed as a collective action, potentially widening participation.
Inside the Discrimination Case
The plaintiff, Derek Mobley, a Black man over 40, says he applied to more than a hundred roles through employers using Workday's recruitment tools. He reports rejections within minutes or overnight, suggesting automated filtering.
His complaint cites the Civil Rights Act, the Americans with Disabilities Act, and the Age Discrimination in Employment Act. Workday denies the allegations. Kelly Trindel, Workday's Chief Responsibility Officer, said the platform does not make hiring decisions or automatically reject candidates and that customers maintain human oversight.
What Workday's AI Actually Does
Workday's platform helps employers manage high volumes of applicants, surface qualified candidates, and standardize requisitions. In 2026, Workday expanded its ecosystem to include the Paradox Conversational Applicant Tracking System for frontline hiring and previewed Frontline Agent, a generative assistant for recruiters and HR teams.
These tools aim to reduce administrative work so people can focus on judgment. The tradeoff: added legal risk if automation influences outcomes tied to protected characteristics. The EEOC has already warned that employers are responsible for discriminatory results from tools they deploy, regardless of vendor claims.
EEOC guidance: assessing adverse impact in AI and algorithms
What HR Should Do Now
Treat AI-driven rankings, filters, and recommendations as part of the hiring decision. Build a defensible process before the next requisition opens.
- Limit or pause automated "pre-screen" filters that reject candidates before any human review, especially for age-adjacent signals (graduation dates, tenure proxies) and disability-related inferences.
- Run structured bias audits on shortlists, screen-outs, and interview recommendations. Track adverse impact ratios and investigate disparities by protected class.
- Document human oversight for each decision point. Who reviewed? What criteria were used? What weight did the tool have versus human judgment?
- Validate job-relatedness. Ensure any screening criteria map to essential functions and are consistent with business necessity.
- Review configurations and vendor defaults. Disable auto-reject rules, tighten knockout questions, and cap the influence of algorithmic scores.
- Upgrade vendor due diligence. Request bias testing results, model cards, data sources, and change logs. Bake audit rights and compliance warranties into contracts.
- Establish an appeal path for candidates. Provide a way to request reconsideration when automation is involved.
- Retain logs and evidence. Keep model outputs, prompts, rankings, and recruiter notes for discovery and compliance reviews.
- Get legal counsel before rolling out or expanding algorithmic screening, especially in jurisdictions adding new AI hiring rules.
Where Your Risk Is Highest
- Automated ranking and screening before human eyes touch the application.
- Knockout questions and rules that act as proxies for age or disability.
- Opaque scoring systems that influence interviews or offers without explainability.
- Vendors or integrations layered onto your ATS that you haven't independently audited.
What Comes Next
If courts find that algorithmic screening counts as employment decision-making, vendor liability becomes real-and so does shared exposure for customers using those tools. Expect more scrutiny across HR tech providers that embed scoring or matching.
Many HR teams will reevaluate vendor settings, pause certain automations, and prioritize explainable AI. Efficiency is still on the table, but transparency and auditability become non-negotiable.
Practical Next Steps This Quarter
- Inventory every automated step in your hiring workflow and flag where a candidate could be excluded without human review.
- Run an adverse impact analysis on your last 6-12 months of requisitions; fix or suspend any high-risk filters.
- Amend vendor contracts to require bias testing, disclosure of material model changes, and cooperation during audits.
- Train recruiters and hiring managers on documenting decisions when AI is in the loop.
If your team needs structured upskilling on AI risk and hiring compliance, explore focused programs for HR roles here: Complete AI Training: Courses by Job.
Bottom line: do not assume vendor compliance equals your compliance. Treat AI outputs as recommendations, keep humans accountable, and be ready to show your work.
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