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2020

The Impossibility Theorem of Machine Fairness -- A Causal Perspective

Saravanakumar, Kailash Karthik

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Understand

With the increasing pervasive use of machine learning in social and economic settings, there has been an interest in the notion of machine bias in the AI community.

Models trained on historic data reflect biases that exist in society and propagated them to the future through their decisions.
There are three prominent metrics of machine fairness used in the community, and it has been shown statistically that it is impossible to satisfy them all at the same time.
This has led to an ambiguity with regards to the definition of fairness.

The Impossibility Theorem of Machine Fairness -- A Causal Perspective · Around

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Fairness definitions explained
Original
Sahil Verma and Julia Rubin · 2007
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Earlier work this paper cites.

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Then

Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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A survey on bias and fairness in machine learning
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Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
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