AI bias isn’t a theoretical concern, it’s already shaping decisions in hiring, lending, healthcare, and everyday digital interactions. And while most conversations frame bias as an ethical dilemma, the real-world impact extends much further: regulatory exposure, legal liability, and long-term reputational damage.In this episode, we break down why bias in AI systems is both a compliance challenge and a trust challenge.From skewed datasets to opaque model logic, we explore how unintended discrimination gets introduced, amplified, and weaponized, and what leaders must do to stay ahead of new regulatory expectations.You’ll learn:- How bias actually enters AI systems (and why it’s harder to detect than you think)- Why “fairness” can’t be a single metric- The growing regulatory landscape around algorithmic accountability- Practical steps security, risk, and compliance leaders can take to mitigate AI bias- Why ethical governance is becoming a core part of enterprise trust programsBias isn’t just about doing the right thing it’s about avoiding the risks you can’t see and the consequences you can’t undo.