A systematization of the literature showing that dataset size and group distance to the decision boundary, not the choice of DP algorithm, are likely the decisive factors in privacy-induced unfairness.
The Impossibility Theorem of Machine Fairness -- A Causal Perspective
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abstract
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. In this report, a causal perspective to the impossibility theorem of fairness is presented along with a causal goal for machine fairness.
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SoK: What Makes Private Learning Unfair?
A systematization of the literature showing that dataset size and group distance to the decision boundary, not the choice of DP algorithm, are likely the decisive factors in privacy-induced unfairness.