fairadapt: Causal Reasoning for Fair Data Pre-processing

• URL: http://arxiv.org/abs/2110.10200v1
• Date: Tue, 19 Oct 2021 18:48:28 GMT
• Title: fairadapt: Causal Reasoning for Fair Data Pre-processing
• Authors: Drago Ple\v{c}ko, Nicolas Bennett, Nicolai Meinshausen
• Abstract summary: This manuscript describes the R-package fairadapt, which implements a causal inference pre-processing method. We discuss appropriate relaxations which assume certain causal pathways from the sensitive attribute to the outcome are not discriminatory.
• Score: 2.1915057426589746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning algorithms are useful for various predictions tasks, but they can also learn how to discriminate, based on gender, race or other sensitive attributes. This realization gave rise to the field of fair machine learning, which aims to measure and mitigate such algorithmic bias. This manuscript describes the R-package fairadapt, which implements a causal inference pre-processing method. By making use of a causal graphical model and the observed data, the method can be used to address hypothetical questions of the form "What would my salary have been, had I been of a different gender/race?". Such individual level counterfactual reasoning can help eliminate discrimination and help justify fair decisions. We also discuss appropriate relaxations which assume certain causal pathways from the sensitive attribute to the outcome are not discriminatory.

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