Multicalibration: Calibration for the (Computationally-Identifiable) Masses
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
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发表时间：
2018-07
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通讯作者：
Úrsula Hébert-Johnson;Michael P. Kim;Omer Reingold;G. Rothblum
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作者：
Úrsula Hébert-Johnson;Michael P. Kim;Omer Reingold;G. Rothblum
我们开发和研究多核算作为机器学习中公平性的新量度，旨在减轻训练时间引入的无意或恶意歧视（甚至是从地面真实数据中）。在指定的计算类别中识别。受保护的群体。我们证明，在许多环境中，这种强烈的歧视概念是可以适当地达到的，并且与准确的预测相符。 ，并说明与不可知论学习模型的紧密联系。
We develop and study multicalibration as a new measure of fairness in machine learning that aims to mitigate inadvertent or malicious discrimination that is introduced at training time (even from ground truth data). Multicalibration guarantees meaningful (calibrated) predictions for every sub-population that can be identified within a specified class of computations. The specified class can be quite rich; in particular, it can contain many overlapping subgroups of a protected group. We demonstrate that in many settings this strong notion of protection from discrimination is provably attainable and aligned with the goal of accurate predictions. Along the way, we present algorithms for learning a multicalibrated predictor, study the computational complexity of this task, and illustrate tight connections to the agnostic learning model.