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Mcboost: Multi-Calibration Boosting for R

MCML Authors

Florian Pfisterer

Dr.

* Former Member

→ Group Bernd Bischl
Statistical Learning and Data Science

Susanne Dandl

Dr.

* Former Member

→ Group Bernd Bischl
Statistical Learning and Data Science

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Abstract

Given the increasing usage of automated prediction systems in the context of high-stakes de- cisions, 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 al- gorithm 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. (2018), Kim et al. (2019), Dwork et al. (2019), Dwork et al. (2020) and Kim et al. (2021).

article PKD+21