# Faking Fairness via Stealthily Biased Sampling

arXiv: https://arxiv.org/abs/1901.08291 · HTML: https://mlsanity.com/paper/1901.08291/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 14 of 14)

- [Adversarial attacks and defenses in explainable artificial intelligence: A survey](https://mlsanity.com/paper/2306.06123/) (2023)
- [Characterizing the risk of fairwashing](https://mlsanity.com/paper/2106.07504/) (2021)
- [Fooling Partial Dependence via Data Poisoning](https://mlsanity.com/paper/2105.12837/) (2021)
- [FairProof : Confidential and Certifiable Fairness for Neural Networks](https://mlsanity.com/paper/2402.12572/) (2024)
- [SoK: Machine Learning Governance](https://mlsanity.com/paper/2109.10870/) (2021)
- [Fool SHAP with Stealthily Biased Sampling](https://mlsanity.com/paper/2205.15419/) (2022)
- [Robust ML Auditing using Prior Knowledge](https://mlsanity.com/paper/2505.04796/) (2025)
- [Survey on AI Ethics: A Socio-technical Perspective](https://mlsanity.com/paper/2311.17228/) (2023)
- [Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attacks](https://mlsanity.com/paper/2507.20708/) (2025)
- [Leveraging Imperfect Sources to Detect Fairwashing in Black-Box Auditing](https://mlsanity.com/paper/2305.13883/) (2023)
- [P2NIA: Privacy-Preserving Non-Iterative Auditing](https://mlsanity.com/paper/2504.00874/) (2025)
- [Manipulation-Proof Oblivious Audits against Deceptive Model Providers](https://mlsanity.com/paper/2608.04365/) (2026)
- [Fair Graph Machine Learning under Adversarial Missingness Processes](https://mlsanity.com/paper/2311.01591/) (2023)
- [Fairness Auditing: Lower Bounds on Company Manipulation](https://mlsanity.com/paper/2608.00568/) (2026)

## Abstract (arXiv)

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.