There is a growing interest in societal concerns in machine learning systems,
especially in fairness. Multicalibration gives a comprehensive methodology to
address group fairness. In this work, we address the multicalibration error and
decouple it from the prediction error. The importance of decoupling the
fairness metric (multicalibration) and the accuracy (prediction error) is due
to the inherent trade-off between the two, and the societal decision regarding
the "right tradeoff" (as imposed many times by regulators). Our work gives
sample complexity bounds for uniform convergence guarantees of multicalibration
error, which implies that regardless of the accuracy, we can guarantee that the
empirical and (true) multicalibration errors are close. We emphasize that our
results: (1) are more general than previous bounds, as they apply to both
agnostic and realizable settings, and do not rely on a specific type of
algorithm (such as deferentially private), (2) improve over previous
multicalibration sample complexity bounds and (3) implies uniform convergence
guarantees for the classical calibration error.

本文探讨了机器学习系统中的社会公正问题，提出了一种多校准方法来解决群体公平性问题。通过解耦公平性度量（多校准）和准确性（预测误差），本研究为多校准误差提供了样本复杂度界限，从而保证了经验的与真实的多校准误差相近。