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), choosing a fairness definition (they conflict, you cannot satisfy all at once), mitigating, and documenting limits in model cards. Applied-AI 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.