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Causal inference for social discrimination reasoning

  • Bilal QureshiEmail author
  • Faisal Kamiran
  • Asim Karim
  • Salvatore Ruggieri
  • Dino Pedreschi
Article

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.

Keywords

Social discrimination Fairness, accountability, and transparency Propensity score Causal analysis 

Notes

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Bilal Qureshi
    • 1
    Email author
  • Faisal Kamiran
    • 2
  • Asim Karim
    • 3
  • Salvatore Ruggieri
    • 4
    • 5
  • Dino Pedreschi
    • 4
    • 5
  1. 1.Addo.aiLahorePakistan
  2. 2.Information Technology University of the PunjabLahorePakistan
  3. 3.Lahore University of Management Sciences (LUMS)LahorePakistan
  4. 4.Università di PisaPisaItaly
  5. 5.ISTI-CNRPisaItaly

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