mcboost: Multi-Calibration Boosting for R
Florian Pfisterer, Christoph A. Kern, Susanne Dandl, Matthew Z. Sun, Michael Kim, Bernd Bischl · The Journal of Open Source Software · 2021
Given the increasing usage of automated prediction systems in the context of high-stakes decisions, a growing body of research focuses on methods for detecting and mitigating biases in algorithmic decision-making.One important framework to audit for and mitigate biases in predictions is that of Multi-Calibration, introduced by Hebert-Johnson et al. (2018).The underlying fairness notion, Multi-Calibration, promotes the idea of multi-group fairness and requires calibrated predictions not only for marginal populations, but also for subpopulations that may be defined by complex intersections of many attributes.A simpler variant of Multi-Calibration, referred to as Multi-Accuracy, requires unbiased predictions for large collections of subpopulations.Hebert-Johnson et al. (2018) proposed a boosting-style algorithm for learning multi-calibrated predictors.Kim et al. (2019) demonstrated how to turn this algorithm into a post-processing strategy to achieve multi-accuracy, demonstrating empirical effectiveness across various domains.This package provides a stable implementation of the multi-calibration algorithm, called MCBoost.In contrast to other Fair ML approaches, MC-Boost does not harm the overall utility of a prediction model, but rather aims at improving calibration and accuracy for large sets of subpopulations post-training.MCBoost comes with strong theoretical guarantees, which have been explored formally in Hebert-Johnson et al.