Bias, Trust & Transparency

​This session examines how algorithmic bias shows up across the talent lifecycle: in résumé screening that filters out certain zip codes or names, in performance scoring that penalizes non-linear career paths, in pay banding tools that anchor to market data built on inequitable baselines.

​Through a practitioner panel and structured case discussion, we'll look at real examples of bias in talent systems: what caused it, who caught it (or didn't), and what organizations are doing to audit for it before it becomes a liability.

​LEARNING OBJECTIVES

​By the end of this session, participants will be able to:

• ​Define algorithmic bias in HR contexts and explain the mechanisms by which AI systems can encode and amplify existing inequities in hiring, performance, and compensation.

• ​Distinguish between disparate impact (outcome-based bias) and disparate treatment (process-based bias) as they apply to AI-driven HR decisions.

• ​Apply a basic bias detection lens to at least one AI-influenced talent process in their organization.

• ​Identify the legal and reputational stakes of unchecked algorithmic bias, including emerging state and local AI employment regulations.

• ​Name two concrete steps their team could take to move from awareness to an inclusive AI audit process.