ML Model Fairness: Measuring and Mitigating Algorithmic Disparities; With Guest: Nick Schmidt
The MLSecOps Podcast
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Accountability, Responsibility, and Fairness in Model Development
This chapter discusses the importance of accountability and responsibility in model development, highlighting the need for testing models on different populations to ensure fairness. It also explores the role of legal regulatory frameworks in enforcing accountability and suggests integrating existing regulatory processes to build a fair ML environment.
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