Group Fairness

Tim Räz

41 citations0/0 files storedACM Conference on Fairness, Accountability, and Transparency (FAccT)

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Ethics and Social Impacts of AI Discrimination and Equality Law Explainable Artificial Intelligence (XAI)

Abstract

This paper critically examines arguments against independence, a measure of group fairness also known as statistical parity and as demographic parity. In recent discussions of fairness in computer science, some have maintained that independence is not a suitable measure of group fairness. This position is at least partially based on two influential papers (Dwork et al., 2012, Hardt et al., 2016) that provide arguments against independence. We revisit these arguments, and we find that the case against independence is rather weak. We also give arguments in favor of independence, showing that it plays a distinctive role in considerations of fairness. Finally, we discuss how to balance different fairness considerations.

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References

2012Fairness through awareness 2016Equality of Opportunity in Supervised Learning 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 2017Algorithmic Decision Making and the Cost of Fairness 2013Learning Fair Representations 2018Fairness in Criminal Justice Risk Assessments: The State of the Art 2000Stanford encyclopedia of philosophy 2016Inherent Trade-Offs in the Fair Determination of Risk Scores

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