Faking Fairness via Stealthily Biased Sampling

TL;DR

This paper demonstrates that malicious decision-makers can create fake fairness in datasets using stealthily biased sampling, making such frauds difficult to detect both theoretically and empirically.

Contribution

It introduces a novel algorithm for stealthily biased sampling that enables fake fairness to be convincingly fabricated in benchmark datasets.

Findings

Fake fairness can be effectively concealed using the proposed sampling method.

Detecting such frauds is theoretically challenging and empirically difficult.

The method undermines the reliability of fairness auditing tools.

Abstract

Auditing fairness of decision-makers is now in high demand. To respond to this social demand, several fairness auditing tools have been developed. The focus of this study is to raise an awareness of the risk of malicious decision-makers who fake fairness by abusing the auditing tools and thereby deceiving the social communities. The question is whether such a fraud of the decision-maker is detectable so that the society can avoid the risk of fake fairness. In this study, we answer this question negatively. We specifically put our focus on a situation where the decision-maker publishes a benchmark dataset as the evidence of his/her fairness and attempts to deceive a person who uses an auditing tool that computes a fairness metric. To assess the (un)detectability of the fraud, we explicitly construct an algorithm, the stealthily biased sampling, that can deliberately construct an evil benchmark dataset via subsampling. We show that the fraud made by the stealthily based sampling is indeed difficult to detect both theoretically and empirically.