Fairness, Bias, and Model Cards

Models can perform unequally across groups, inheriting and amplifying bias in the data, which is a harm and, in regulated domains, illegal. Fairness work means measuring per-group performance (not just aggregate), settling on a fairness definition (they conflict, you cannot satisfy all at once), mitigating, and documenting limits in model cards. AI, ML, and GenAI interviews probe it because aggregate accuracy hides subgroup failures, and shipping a biased model in hiring, lending, or healthcare is a serious, sometimes-unlawful failure.