# Calibration for the (Computationally-Identifiable) Masses

@article{HbertJohnson2017CalibrationFT, title={Calibration for the (Computationally-Identifiable) Masses}, author={{\'U}rsula H{\'e}bert-Johnson and Michael P. Kim and Omer Reingold and Guy N. Rothblum}, journal={ArXiv}, year={2017}, volume={abs/1711.08513} }

As algorithms increasingly inform and influence decisions made about individuals, it becomes increasingly important to address concerns that these algorithms might be discriminatory. The output of an algorithm can be discriminatory for many reasons, most notably: (1) the data used to train the algorithm might be biased (in various ways) to favor certain populations over others; (2) the analysis of this training data might inadvertently or maliciously introduce biases that are not borne out in…

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