brandeis-machine-learning/awesome-ml-fairness
Papers and online resources related to machine learning fairness
This resource provides a comprehensive list of research papers and online materials focused on fairness in machine learning. It helps researchers, policymakers, and practitioners understand how to identify, measure, and mitigate bias in AI systems. It compiles academic work, case studies, and practical guides on various aspects of algorithmic fairness, offering insights into responsible AI development.
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Use this if you are a researcher, policymaker, or practitioner who needs to understand the current state of research and practical applications regarding fairness in machine learning.
Not ideal if you are looking for an interactive tool or software library to directly implement fairness techniques without diving into academic literature.
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Apache-2.0
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Last pushed
May 11, 2023
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