fairadapt: Causal Reasoning for Fair Data Preprocessing

fairadapt: Causal Reasoning for Fair Data Preprocessing

algorithmic fairness, causal inference, machine learning.

Machine learning algorithms are useful for various prediction 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 recognize, quantify
and ultimately mitigate such algorithmic bias. This manuscript describes the R package
fairadapt, which implements a causal inference preprocessing method. By making use of
a causal graphical model alongside 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 that certain causal pathways from the sensitive attribute to the outcome
are not discriminatory.

Drago Plečko

https://www.jstatsoft.org/article/view/v110i04

ETH Zürich

May 2024

Fajar bagus W

PDF

English

Text