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This lecture explores the critical challenges of algorithmic fairness as artificial intelligence increasingly influences decisions in hiring, education, healthcare, and social services. Examine how AI systems, while promising efficiency and objectivity, can perpetuate and amplify societal biases embedded in training data. Learn about the origins of algorithmic bias and its impact on machine learning algorithms, along with the main approaches used to define, study, and enforce fairness in automated decision-making. Delve into the statistical fairness framework with a focus on fair regression problems, particularly exploring the Demographic Parity criterion and examining the relationship between optimal predictions in fair classification versus fair regression contexts. Presented by Solenne Gaucher from École Polytechnique, this 53-minute talk provides valuable insights for researchers and practitioners concerned with ethical AI implementation.
