When algorithms reject job candidates in seconds or deny loans to qualified families, who's responsible? This episode tackles the ethics of AI decision-making in hiring, lending, and insurance, sparked by a listener's question about automated bias.

We explore how AI creates new discrimination patterns that don't match human biases, why removing protected characteristics doesn't prevent discrimination, and the real business costs of biased algorithms—including Colorado's $35M per-violation fines.

Learn the three essential steps every executive should take: Map where AI touches decisions, Measure differential impact using simple tests like the four-fifths rule, and Manage with human checkpoints and audit trails. We also reveal surprising research showing fairness can actually improve business outcomes, with mortgage algorithms increasing minority approval rates 5-13% without raising default risk.

Whether you're deploying AI or using vendor systems, discover why "no audit rights, no deployment" should be your new house rule.

Disclaimer: This episode is AI-powered—researched, scripted, and voiced—using publicly available real news and data. For info only, not financial or legal advice. Our voices are AI-generated; minor inconsistencies are part of the tech's growth curve.