Bias mitigation reduces unfair biases in model training, data, and outputs affecting demographic groups. Bias types: Representation (training data imbalance), association (stereotypical correlations), selection (biased data collection), measurement (inconsistent labeling). Training-time mitigation: Data augmentation to balance representation, counterfactual data augmentation, adversarial debiasing (train to be invariant to protected attributes), fair loss functions. Inference-time mitigation: Output re-calibration across groups, filtered decoding to avoid stereotypes, prompt-based steering. Data approaches: Audit training data for representation, remove biased correlations, collect from diverse sources. Evaluation: Test across demographic slices, use fairness benchmarks (BBQ, WinoBias), red-teaming for bias. Challenges: Defining "fair", intersectionality, lack of demographic labels, cultural variation in bias. Transparency: Document known biases, model cards, intended use guidelines. Trade-offs: Fairness metrics can conflict, may reduce overall accuracy, requires ongoing monitoring. Best practices: Continuous evaluation, diverse evaluation teams, stakeholder input. Essential for responsible AI deployment.