方法证据记录
Fairness-Aware ML
Fairness-Aware Machine Learning is a family of techniques that train, constrain, or post-process predictive models so that their error rates or outcomes are equitable across protected demographic groups such as race, gender, or age. The foundational framework of equalized odds and equality of opportunity was formalized by Moritz Hardt, Eric Price, and Nati Srebro in their landmark 2016 NeurIPS paper, establishing rigorous statistical criteria for non-discriminatory classifiers.
源记录
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Fairness-Aware Machine Learning
分类方法记录 · ml-model / machine-learning
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