Example Concept: Bias mitigation in automated decision systems involves identifying potential sources of bias in data and algorithms, applying fairness metrics to evaluate model outcomes, and using techniques like re-sampling, re-weighting, or adversarial debiasing to correct imbalances. Organizations should also document these processes in model cards to ensure transparency and accountability.