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Apr, 2021
一个公理化的理论:可证明公平的以福利为中心的机器学习
An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning
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Cyrus Cousins
TL;DR
本研究提出一种公平机器学习的替代方案,通过最小化每个群体的风险值来测量整体社会伤害(而不是福利)的互补度量malfare,并将公平机器学习建模为malfare最小化问题,这可转化为FPAC学习,为众多公平机器学习模型提供了统计和计算效率保证的具体训练算法和严格的泛化保证。
Abstract
We address an inherent difficulty in welfare-theoretic
fair machine learning
, proposing an equivalently-axiomatically justified alternative, and studying the resulting computational and statistical learning questions.
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