Integrating Fairness into Machine Learning Pipelines: A Necessity

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This presentation offers an in-depth exploration of fair learning within AI, beginning with a foundational understanding of its definition and significance in the field of machine learning. We will delve into the ethical motivations for fairness, the social implications, and relevant legislative requirements. The discussion will identify various types of fairness harms, including data and model biases, and propose mitigation strategies through pre-, in-, and post-processing techniques....