A federal judge has rebuffed Workday’s claim that it cannot be held liable under California anti-discrimination laws when its tools are used to screen (and potentially reject) job candidates in other states.

This week, US District Judge Rita Lin indicated that she will likely allow additional state discrimination claims against Workday to move forward. This would significantly expand the closely-watched case and likely ratchet up scrutiny of AI recruiting tools and their potentially inherent biases when it comes to age, race, sex, disabilities, and other factors.

Further, it could indicate that, even if a company is not the final employer, it may be held liable if its tools materially influence who gets rejected. This could set new legal standards for AI hiring systems, and have implications across industries, experts note.

“This case reinforces the importance of actually managing AI risks,” said Valence Howden, advisory fellow at Info-Tech Research Group. “If an AI-enabled model or ATS [Applicant Tracking System] is making decisions based on historical information, it can raise questions about whether bias in outcomes and datasets has been properly addressed.”

The case so far

Mobley v. Workday, Inc. alleges that Workday’s AI screening tools discriminate against job seekers based on age, race, and disability. The suit was filed in 2024 in the US District Court of California by Derek Mobley, a Black disabled man over 40, who claimed Workday’s algorithms continually screened him out as he applied for more than 100 positions on the platform.

The claims alleged discrimination prohibited by several US and California statutes: Race and sex under the Civil Rights Act of 1964 (Title VII); disability under the Americans with Disabilities Act of 1990 (ADA); age under the Age Discrimination in Employment Act of 1967 (ADEA); and race, gender, and age under California Fair Employment and Housing Act (FEHA).

Specifically, the suit centered around Workday’s use of automated, algorithm-driven tools for applicant screening. It alleged that these systems rely on historical data and statistical modeling that can make them susceptible to existing biases, even if protected characteristics like race, age, sex, or disability are not explicitly provided.

Bias may enter these systems in different ways, the plaintiffs argued, including via training data, model design, and evaluation criteria for candidate fit. The system could reproduce discriminatory outcomes by making correlations from data. For instance, years of experience on a resumé may indicate age; long employment gaps may infer a disability or caregiving responsibilities; educational and institutional affiliations could reflect race.

Workday has argued that it is not subject to liability under employment statutes because it does not qualify as the job applicants’ “employer.” But federal judges have allowed key parts of the lawsuit to move forward, ruling that Workday could potentially be treated as an employer’s “agent” for the purposes of anti-discrimination law.

The latest dispute centers on FEHA. According to legal sources, the California statute is among the strongest anti-discrimination laws in the US, in many cases providing broader protections than federal employment laws.

Workday asked the court to dismiss claims brought under California law, saying FEHA should not apply to the hiring decisions of out-of-state employers and applicants. The company’s lawyers argued that enforcing this would effectively allow California law to supersede that of other states, just because a company used their platform.

But Lin disagreed, saying FEHA does apply, and in fact, Workday is directly liable for its “own engagement in FEHA-regulated activities on the employer’s behalf.” Holding businesses liable for “their own discriminatory conduct” is within the scope and purposes of FEHA and consistent with public policy.

However, the issue is still to be decided; Lin did not indicate when she would release a final ruling.

Workday’s defense

A Workday spokesperson called the claims in the suit “false.”

“Workday’s AI recruiting tools don’t make hiring decisions and are designed with human oversight at their core,” the spokesperson told CIO. “Our technology looks only at job qualifications, not protected traits like race, age, or disability. We rigorously test our products as part of our responsible AI program to confirm our tools do not harm protected groups.”

Workday’s platform is meant to provide insights on how well a candidate’s qualifications match the requirements of a posted job, the company said. Those tools focus only on qualifications listed in a candidate’s application, which are compared to qualifications identified by the employer as important for the job.

Workday’s Chief Responsible AI Officer Kelly Trindel said its AI does not make employment decisions, automatically reject candidates, or determine who gets a job; further, she said, there is no evidence that the company’s tools result in harm to protected groups.

Trindel, who is former chief analyst of the Equal Employment Opportunity Commission (EEOC), leads a dedicated team composed of psychologists and PhD-level data scientists whose sole focus is to ensure that its AI is “responsible, fair, and ethical.” She said that the company’s AI systems undergo ongoing reviews throughout their lifecycle to help prevent unintended consequences, and Workday is “committed to accountability, transparency, and trust,” and invests “significant resources” into identifying and mitigating bias.

Further, she said, Workday has a company-wide commitment to ethical AI, and an independently-evaluated AI governance program based on standards from the National Institute of Standards and Technology (NIST) and the International Standards Organization (ISO).

“Workday builds AI to support people, not replace them, and this is of particular importance when it comes to hiring,” Trindel noted. Its platform is designed to help employers “manage high-volume processes more efficiently, surface relevant information, and reduce administrative work so teams can spend more time applying their expertise and judgement to hiring decisions.”

What this means for enterprise leaders

Workday isn’t alone in its legal challenges; other AI hiring tools are also being scrutinized over their methodologies, algorithms, and data-collecting practices. Eightfold, for one, is also facing a California class action lawsuit alleging that its tools unfairly rely on job candidates’ online data to predict whether they’d be a good fit for a position.

This means that enterprises, who are already feeling increased pressure to document hiring decisions, conduct AI bias audits, and maintain human oversight in recruitment and hiring, must be even more diligent in their vetting of AI tools.

Organizations must be actively defining how these recruitment tools should work, identifying bias in their algorithms, and setting up structures to test for bias across the tools’ decision-making logic, Info-Tech’s Howden advised.

“Validation of non-biased outcomes also needs to be active and ongoing, rather than a point-in-time exercise,” he said.

While Workday and others say human oversight is paramount, “it’s hard to incorporate humans into the process if the platform does the weeding out before humans have the ability to intervene,” Howden pointed out.

Discriminatory biases can exist in past hiring decisions, so it’s easy to forget that AI can “emulate and adapt those biases as part of its perspective,” he said. That includes how AI looks at language: Different cultures use different phrasing, and AI can capture that and use it to exclude candidates.

Ultimately, he called the case a “cautionary tale” illustrating how lightly some organizations have been treating AI risk. It also highlights the urgency involved in building out more advanced enterprise risk practices, “rather than relying on the limited capabilities they may have employed up until now.”

This article originally appeared on CIO.com.

A US federal judge has ruled that the Trump administration’s $100,000 fee on new H-1B visa petitions was unlawful, giving technology companies temporary relief from a policy that threatened to raise the cost of hiring foreign skilled workers.

The decision removes, at least for now, a major cost burden for employers that use the H-1B program to fill roles in domains including software development, cloud computing, data science, and AI.

US District Judge Leo Sorokin in Boston found that the fee functioned as a tax that the administration did not have authority to impose without congressional approval. The ruling came in a lawsuit brought by 20 Democratic state attorneys general challenging the fee.

Standard employer costs for H-1B petitions typically range from about $2,000 to $5,000, making the proposed $100,000 payment a sharp increase for companies seeking foreign talent.

The ruling is unlikely to end uncertainty for employers, with the Trump administration expected to appeal. But it could allow companies that had paused international hiring plans to resume normal recruitment for the upcoming H-1B cycle, said Pareekh Jain, CEO of Pareekh Consulting. Still, he said, employers should remain cautious because the legal and policy concerns are likely to continue.

“This provides breathing room for CIOs, even though it’s temporary,” said Neil Shah, vice president for research and partner at Counterpoint Research. “They should make the necessary contingency plans, whether that means doing more with less by leveraging AI or relying more on local talent.”

How companies may rethink hiring

If higher H-1B costs return in another form, CIOs will have to be more selective about sponsorship, weighing the added cost against the strategic value of the role and the long-term potential of the employee, Shah said.

“Ultimately, the decision comes down to business unit P&L: whether the unit can absorb the cost of acquiring the talent for that role,” Shah added.

That uncertainty could also lead CIOs to compete for talent from other companies, potentially driving up salaries for skilled workers. Some CIOs may conclude that paying a one-time $100,000 fee, amortized over the employee’s tenure, is still more cost-effective than engaging in a bidding war for scarce local talent.

Danish Faruqui, CEO of Fab Economics, said that CIOs may reserve H-1B sponsorship for a narrower set of mission-critical roles if costs increase.

“If there is such a financial burden, CIOs will justify sponsoring very specific roles,” Faruqui said. “These would be principal enterprise architects, AI, ML, and deep-tech researchers, senior product managers, and regulatory and compliance experts.”

More routine or project-based roles are likely to be treated differently, Faruqui said.

“Junior to mid-level software engineers, entry-level business analysts, and entry-level data scientists would shift from H-1B to domestic hiring,” Faruqui said. “Cloud migration, DevOps, ERP, and CRM implementation could be done through contractors or consulting firms, while QA, product testing, tier-one help desk support, and legacy maintenance are roles that CIOs could prioritize for automation.”

Who would be most affected?

Startups, smaller companies, and enterprise IT departments would have faced the greatest pressure from the fee and stand to benefit most from the ruling, Jain said.

Large technology companies would have been better placed to absorb the $100,000 cost, he said. Meanwhile, companies with mature offshore delivery models may be less likely to increase their reliance on H-1B hiring.

The article originally appeared on CIO.

A research project examining AI-driven recruitment hires across the US has revealed a systemic racial bias.

Researchers from Stanford University found a startling pattern of racial disparities when looking at the interview offers resulting from 4 million job applications submitted to 156 employers. The situation is aggravated by the “monoculture” in AI hiring software: More than 90% of US employers are screening job applicants with software, with 60% of Fortune 500 companies using the same tool, HireVue, the researchers found.

Applicants who applied to multiple companies using AI had all their applications rejected more often than would be expected if each company’s screening methods were independent. They calculated that Black and Asian candidates were rejected in greater numbers than baseline figures would suggest. According to the survey, 29,000 more Asians would have been interviewed if AI had not been deployed.

The researchers are concerned about the way in which AI is being used. “AI screening tools bring together three properties that should not co-exist in high-stakes decision-making: They are pervasively adopted, highly consequential, and opaque to the public,” they said in a news release presenting their work.

The effect of this will lead to workplaces dominated by a monoculture which may not be beneficial for companies going forward.

This article first appeared on CIO.