AI Bias: Identifying and Mitigating It in Your Pipeline

AI bias often enters through unrepresentative training data or proxy variables that correlate with protected attributes. Measure disparate impact by computing false positive and false negative rates per group — significant differences signal bias. Mitigate it through re-sampling, re-weighting, adversarial debiasing, or post-processing prediction thresholds per group.