As machine learning (ML) systems increasingly shape decisions in finance, healthcare, hiring, and justice, questions of fairness are no longer philosophical or peripheral; they’re foundational. While model accuracy and performance still dominate technical discussions, they alone don’t guarantee ethical or responsible AI. In fact, a highly accurate model can still be deeply unfair if it’s built on biased data or deployed without regard to disparate impacts.
Fairness in ML is a multifaceted and often misunderstood problem. It’s not just about intent, it’s about outcomes. A seemingly neutral model can encode historical bias or reflect systemic inequalities, producing skewed decisions that affect real lives. That’s why fairness audits are essential, not as one-time checks, but as continuous, technical practices baked into the machine learning lifecycle.