AI and Employment Fairness: How Design, Audits, and Compliance Work Together

Most conversations about AI and bias start with the audit results. The more important question is whether the tool was designed fairly in the first place.

In this session, Tina Shah Paikeday, Responsible AI Senior Adviser at Findem, and Jeffrey Pole, Co-Founder and CEO of Warden AI, cover both sides of the problem: what responsible AI design looks like in hiring, and what rigorous compliance auditing actually requires. Tina draws on cognitive bias research to explain why some AI tools accelerate the biases humans already carry — and why others don't.

Jeffrey brings audit data showing where bias is appearing in hiring today, what the regulatory landscape looks like as state-level legislation accelerates, and how purpose-built AI systems compare to human-only processes on fairness measures.

Watch to leave with a sharper framework for evaluating the AI in your hiring stack — and better questions to ask your vendors.

• Why AI architecture — not bias-mitigation programs layered on top — determines whether a hiring tool produces fair outcomes
• What position-level audit methodology and the Stanford algorithmic monoculture study mean for how you read vendor audit results
• How purpose-built AI systems compare to human-powered hiring on bias, according to Warden AI's meta-analysis across gender and race/ethnicity data