Example Concept: Bias mitigation in AI models can be achieved by employing techniques such as re-sampling, re-weighting, or adversarial debiasing during training. Additionally, fairness metrics like demographic parity, equal opportunity, and disparate impact can be used to evaluate and ensure model fairness across different groups. Regular audits and transparency tools like model cards help document the model's behavior and its impact on various demographic groups, ensuring ongoing accountability and bias reduction.