Independent, third-party bias audits for AI hiring tools. Mathematical proof of fairness for NYC LL 144 and federal courts.
Class action settlements in AI bias and privacy cases
of AI resume screeners found to prefer white-associated names
per violation for using unaudited automated hiring tools
The legal landscape has shifted. Mobley v. Workday established that AI vendors can be held directly liable for employment discrimination. NYC Local Law 144 requires annual bias audits for any automated employment decision tool. Research proving systemic bias in AI resume screeners gives plaintiffs ammunition.
This isn't about good intentions. It's about mathematical proof. When the lawsuit arrives, "we thought it was fair" won't hold up. You need documented, third-party validation.
Lucid's Hiring Fairness Auditor runs rigorous disparate impact testing on your AI hiring tools. You get the independent bias audit required by NYC LL 144 and the mathematical documentation needed to defend against federal discrimination claims.
Automated disparate impact testing across all protected categories—race, gender, age, disability status.
Validate fairness without exposing actual candidate data, using statistically valid synthetic populations.
All testing runs in a hardware-secured environment (TEE), ensuring results are mathematically valid and unmanipulated.
Generates the Public Summary required by Local Law 144
Cryptographically signed audit certificates for litigation defense
Testing across race, gender, age, national origin, disability
Get the mathematical proof of fairness before plaintiffs' attorneys come asking.