Techniques and methodologies to identify, measure, and reduce algorithmic bias and distortions in AI systems. Includes data balancing strategies, fairness testing, model auditing, pre-processing and post-processing debiasing, fairness metrics, and approaches to ensure non-discriminatory AI decisions regarding gender, ethnicity, age, and other protected characteristics.