Ethical AI: Bias Detection

Understanding and mitigating bias in deep learning is fundamental to building fair, inclusive AI systems. Bias can manifest at different stages—unbalanced class distributions, misrepresentative training data, and flawed model assumptions. Common mitigation strategies include pre-processing (e.g., re-sampling or re-weighting data), in-processing (e.g., fairness-aware training and adversarial debiasing), and post-processing adjustments to outputs. Regular auditing, monitoring, and explainability are also vital for maintaining fairness over time, supported by tools like AIF360 and fairness dashboards. (in progress)