Example Concept: Algorithmic bias mitigation involves identifying potential biases in data and models, applying fairness metrics to evaluate these biases, and implementing corrective measures such as re-sampling, re-weighting, or using fairness-aware algorithms. Organizations should also maintain transparency by documenting these processes in model cards, which provide stakeholders with clear insights into model performance and limitations.