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 …

This important text and reference for researchers and students in machine learning, game
theory, statistics and information theory offers a comprehensive treatment of the problem of …

Probabilistic forecasts of continuous variables take the form of predictive densities or
predictive cumulative distribution functions. We propose a diagnostic approach to the …

Linear pooling is by far the most popular method for combining probability forecasts.
However, any non-trivial weighted average of two or more distinct, calibrated probability …

Prediction algorithms assign numbers to individuals that are popularly understood as
individual “probabilities”—what is the probability of 5-year survival after cancer diagnosis …

We exhibit a large class of simple rules of behavior, which we call adaptive heuristics, and
show that they generate rational behavior in the long run. These adaptive heuristics are …

Proper scoring rules provide convenient and highly efficient tools for incentive-compatible
elicitations of subjective beliefs. As traditionally used, however, they are valid only under …

We show how to achieve the notion of" multicalibration" from Hebert-Johnson et al.(2018)
not just for means, but also for variances and other higher moments. Informally, this means …

A recent line of work has shown a surprising connection between multicalibration, a multi-
group fairness notion, and omniprediction, a learning paradigm that provides simultaneous …

Multi-calibration is a powerful and evolving concept originating in the field of algorithmic
fairness. For a predictor $ f $ that estimates the outcome $ y $ given covariates $ x $, and for …