Bias Mitigation

Bias mitigation involves identifying and addressing biases that may exist in training data or model predictions. These biases can lead to unfair or inaccurate outcomes, particularly affecting marginalized groups. By employing techniques like data augmentation, re-sampling, or algorithmic adjustments, developers can enhance the fairness and ethical implications of AI systems.