# Politics of Adversarial Machine Learning

arXiv: https://arxiv.org/abs/2002.05648 · HTML: https://mlsanity.com/paper/2002.05648/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [Algorithmic Collective Action in Machine Learning](https://mlsanity.com/paper/2302.04262/) (2023)
- [FACE-AUDITOR: Data Auditing in Facial Recognition Systems](https://mlsanity.com/paper/2304.02782/) (2023)
- [Adversarial Robustness for Tabular Data through Cost and Utility Awareness](https://mlsanity.com/paper/2208.13058/) (2022)
- [Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally](https://mlsanity.com/paper/2310.03614/) (2023)
- [Statistical Collusion by Collectives on Learning Platforms](https://mlsanity.com/paper/2502.04879/) (2025)
- [Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks](https://mlsanity.com/paper/2107.10302/) (2021)
- [Identifying Adversarial Attacks on Text Classifiers](https://mlsanity.com/paper/2201.08555/) (2022)
- [Position: Certified Robustness Does Not (Yet) Imply Model Security](https://mlsanity.com/paper/2506.13024/) (2025)
- [Online Algorithmic Recourse by Collective Action](https://mlsanity.com/paper/2401.00055/) (2023)
- [Catastrophic overfitting can be induced with discriminative non-robust features](https://mlsanity.com/paper/2206.08242/) (2022)
- [Addressing Privacy Threats from Machine Learning](https://mlsanity.com/paper/2111.04439/) (2021)
- [The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI](https://mlsanity.com/paper/2107.14052/) (2021)

## Abstract (arXiv)

In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy them, creating risks for civil liberties and human rights. In this paper, we draw on insights from science and technology studies, anthropology, and human rights literature, to inform how defenses against adversarial attacks can be used to suppress dissent and limit attempts to investigate machine learning systems. To make this concrete, we use real-world examples of how attacks such as perturbation, model inversion, or membership inference can be used for socially desirable ends. Although the predictions of this analysis may seem dire, there is hope. Efforts to address human rights concerns in the commercial spyware industry provide guidance for similar measures to ensure ML systems serve democratic, not authoritarian ends