Trusted Data 2019
DOI: 10.7551/mitpress/12439.003.0013
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Active Fairness in Algorithmic Decision-Making

Abstract: Society increasingly relies on machine learning models for automated decision making. Yet, efficiency gains from automation have come paired with concern for algorithmic discrimination that can systematize inequality. Recent work has proposed optimal postprocessing methods that randomize classification decisions for a fraction of individuals, in order to achieve fairness measures related to parity in errors and calibration. These methods, however, have raised concern due to the information inefficiency, intra-… Show more

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