Title: On Fairness and Calibration,
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 groups while maintaining calibrated
probability estimates. We show that calibration is compatible only with a
single error constraint (i.e. equal false-negatives rates across groups), and
show that any algorithm that satisfies this relaxation is no better than
randomizing a percentage of predictions for an existing classifier. These
unsettling findings, which extend and generalize existing results, are
empirically confirmed on several datasets.