The Mobley v. Workday case has sparked concern across the HR and AI community. While headlines focus on allegations of algorithmic bias, our recent panel highlighted a deeper issue:
Too many decisions are being driven by assumptions. Not enough are grounded in evidence.
We brought together a group of experts to unpack the case and its wider implications:
- Jung McCann, Chief Legal Officer at Greenhouse
- Sarah Smart, co-founder of Horizon Human and former Global Head of TA Product at JPMorgan Chase
- Kyle Lagunas, founder of Kyle & Company, moderated the session
- Jeff Pole, CEO of Warden AI (me), provided the audit and measurement perspective
Here’s how we see assumptions getting in the way, and what we need to do differently.
1. Assuming bias is already proven
Many people are treating the Mobley case as confirmation that Workday’s AI system is biased. But we’re still at the allegation stage. As Jung put it:
“No evidence has been produced yet. This case is years away from being resolved, and we’re still in a heated discovery battle over what data is even available.”
Jumping to conclusions before evidence is introduced (whether as buyers, vendors, or regulators) is risky and reactionary.
2. Assuming all AI is inherently biased
There’s a widespread assumption that because AI is trained on historical data (and historical data reflects human bias), all AI must be biased too.
But as I shared during the session:
“We’ve seen in many cases that AI systems, when tested properly, are actually less biased than human decision-making.”
In fact, our own data suggests that across a range of hiring scenarios, AI systems audited for bias tend to perform more fairly on average than their human counterparts.
That fairness isn’t guaranteed. But it’s achievable when we treat bias measurement as a foundational requirement.
3. Assuming humans are the gold standard
There’s a persistent belief that human decision-making is the safer fallback. But that assumes human judgment is consistent, fair, or even measurable. It often isn’t.
Sarah put it plainly:
“We couldn’t tell if bias was in the system or in our recruiters. We had to assemble a cross-functional team (legal, data scientists, architects) just to start untangling it.”
AI gets held to a high standard. Human decision-making rarely does. But if we don’t test either, we’re managing neither.
4. Assuming vendor claims are enough
Sarah shared a telling example. Her team implemented an AI matching tool based on vendor promises, only to discover later it wasn’t doing what they thought.
“We assumed it was smart AI. It turned out to be basic keyword matching, not a learning algorithm. And when we dug deeper, it was hard to tell if any of the reporting could actually show whether bias was happening or not.”
Vendor branding shouldn’t replace scrutiny. If you don’t validate how a tool performs in your environment, you’re running on trust, not facts.
5. Assuming AI is safe to use because it has an audit
Many vendors (and customers) assume that if an audit has been done, the system must be fair. But most audits only cover race and gender. Even those are often annual.
“Less than 5% of third-party audits we see include characteristics like age or disability,” I noted. “That’s a major gap. And Mobley, focused on age discrimination, shows why it matters.”
Without comprehensive and regular testing, there’s no reliable picture of how a system behaves and no defensible evidence if it’s challenged.
How to Prepare for the Next "Mobley" Case
The Mobley v. Workday case is a wake-up call for any company using automated screening. To protect your brand and bottom line, we recommend a three-step evidence-based approach:
- Inventory Your AI: Identify every tool that assists in "discretionary decision-making."
- Verify Vendor Claims: Do not take "bias-free" marketing at face value; demand an independent audit summary.
- Implement Continuous Monitoring: Annual audits are the legal minimum, but high-volume platforms require real-time bias detection to catch shifts in data.
The takeaway: you can’t manage what you don’t measure
What Mobley v. Workday really underscores is that lack of measurement and evidence is the core risk. Not AI itself.
We don’t need to pause AI adoption. But we do need to pause assumptions. Bias in hiring can’t be assessed (let alone reduced) without robust measurement. And right now, most organizations simply don’t have the data. This lack of data is the primary conversion blocker for HR tech vendors and enterprise employers. Warden AI provides the infrastructure to bridge this gap. Who it's for: Our services are designed for legal teams, DE&I officers, and product managers who need to prove their tools are safe for market. What’s included: We offer a full-scale audit that covers race, gender, and—crucially—age and disability, which were central to the Mobley case. Pricing: We offer tiered pricing based on the number of models being audited, ensuring that even growth-stage startups can afford enterprise-grade protection. Choosing Warden AI means choosing a partner that understands the legal nuances of the latest AI litigation trends.
Fairness in AI isn’t automatic. But it’s not hypothetical either. The data shows it’s possible.
If we want to build fairer hiring systems, we have to start by measuring them.
FAQ
Does the Mobley v. Workday case mean AI is illegal? No, but it highlights that companies can be held liable for "indirect" discrimination if they cannot prove their systems are fair.
How does a third-party audit help in court? An independent audit provides "good faith" evidence that you have taken reasonable steps to identify and mitigate bias, which is a key defense in employment litigation.
Does Warden AI audit for age and disability? Yes. Unlike many competitors who only fulfill the bare minimum of NYC LL 144, we offer expanded audits to cover the categories most frequently cited in recent lawsuits.
What is the first step to getting audited? The process begins with a Data Readiness Assessment to ensure your historical hiring data is sufficient for a statistically valid audit.
