Example Concept: Bias detection and mitigation involve using fairness metrics such as demographic parity, equalized odds, and disparate impact to evaluate model outcomes. Tools like fairness dashboards and bias detection libraries can help identify and visualize biases in training data and model predictions. Regular audits and retraining with balanced datasets are essential to minimize bias and ensure fairness.