Algorithmic Bias and Fairness

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This lecture explores algorithmic bias and fairness, beginning with definitions, significance, and real-world implications. It delves into key literature findings, including bias mitigation techniques and sensitive attributes. Challenges such as intersectionality, legacy data, and causal inference will be examined. Emerging trends in statistical parity, fairness metric standardization, and model explainability are discussed, providing a comprehensive understanding. The session concludes with...