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What our AI audits reveal about AI bias, vendor performance, and best practices
Key findings
What the data shows
75% of HR leaders say bias is a top concern when adopting AI
15% of AI systems fail to meet fairness metrics for one or more demographic group
AI scores 0.94 vs 0.67 for humans, outperforming on average across fairness metrics
AI is up to 45% more fair than humans for women and racial minority candidates
About this report
At a time when headlines are dominating the news; allegations are playing out in court; and candidate perception is at an all-time low; the debate around AI bias has never been louder.
As an independent auditor working across the talent tech landscape, Warden AI has a unique vantage point on this issue.
With permission from our customers, we’ve compiled this report to move the conversation from speculation to evidence and provide a clear, data-backed view of the state of AI bias in hiring.
This report brings together:
- 150+ AI audits across TA tech vendors
- 1 million test samples+ from real-world AI systems
- 100+ survey responses from tech vendors and practitioners
Contributor voices
Why responsible AI matters more than ever
Bias in AI hiring is real, but it’s also measurable, manageable, and mitigatable. This report moves us past assumptions and toward evidence.
We are right to worry about AI bias, but we should not forget that the baseline, human only judgment, is far from bias-free.
Warden's Platform
Certifying AI systems for fairness and compliance
AI can be fairer and more consistent than human decision-making if designed and used carefully.
We help HR develop, deploy, and defend the AI systems that meet that standard.
Continuous auditing
Ongoing technicals audits to catch risks early and provide assurance that the AI safe to use at all times.
Audit all protected classes
Audits mapped to AI regulations like NYC LL144, the EU AI Act, and Colorado SB205, with coverage across all protected classes under civil rights law.
Guard against legal claims
Each audit is logged with secure timestamps and a frozen snapshot of the dataset, traceable to the exact period of any potential claim.
