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Journal of Intelligent Information Systems

Published in:

07-11-2019

Causal inference for social discrimination reasoning

Authors: Bilal Qureshi, Faisal Kamiran, Asim Karim, Salvatore Ruggieri, Dino Pedreschi

Published in: Journal of Intelligent Information Systems | Issue 2/2020

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Abstract

The discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis, a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms.

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See e.g., the AI Now report on Discriminating Systems, April 2019 (https://ainowinstitute.org/discriminatingsystems).

Code at: https://github.com/anam-zahid/Causal-inference-for-social-discrimination-reasoning

http://archive.ics.uci.edu/ml

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Title: Causal inference for social discrimination reasoning
Authors: Bilal Qureshi
Faisal Kamiran
Asim Karim
Salvatore Ruggieri
Dino Pedreschi
Publication date: 07-11-2019
Publisher: Springer US
Published in: Journal of Intelligent Information Systems / Issue 2/2020
Print ISSN: 0925-9902
Electronic ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-019-00580-x

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