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Feb, 2021
评估不确定和不完整信息下机器学习模型的公平性
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
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Pranjal Awasthi, Alex Beutel, Matthaeus Kleindessner, Jamie Morgenstern, Xuezhi Wang
TL;DR
研究公平分类器培训和评估的挑战,探讨对数据点的敏感属性信息和标签信息的公平性指标依赖性,以及在数据缺乏的情况下如何训练和使用属性分类器进行偏差估计。
Abstract
Training and evaluation of
fair classifiers
is a challenging problem. This is partly due to the fact that most fairness metrics of interest depend on both the
sensitive attribute information
and label information
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