  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.
