On Fairness and Calibration

neural information processing systems, pp. 5684-5693, 2017.

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Abstract:

The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models. This has motivated a growing line of work on what it means for a classification procedure to be fair. In this paper, we investigate the tension between minimizing error disparity across different population...More

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