# Contestable Black Boxes

arXiv: https://arxiv.org/abs/2006.05133 · HTML: https://mlsanity.com/paper/2006.05133/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Contestable AI needs Computational Argumentation](https://mlsanity.com/paper/2405.10729/) (2024)
- [Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions](https://mlsanity.com/paper/2107.09546/) (2021)
- [Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)](https://mlsanity.com/paper/2205.09787/) (2022)
- [Clash of the Explainers: Argumentation for Context-Appropriate Explanations](https://mlsanity.com/paper/2312.07635/) (2023)
- [Contesting Artificial Moral Agents](https://mlsanity.com/paper/2603.00066/) (2026)
- [Explainable AI Isn't Enough! Rethinking Algorithmic Contestability](https://mlsanity.com/paper/2605.16041/) (2026)

## Abstract (arXiv)

The right to contest a decision with consequences on individuals or the society is a well-established democratic right. Despite this right also being explicitly included in GDPR in reference to automated decision-making, its study seems to have received much less attention in the AI literature compared, for example, to the right for explanation. This paper investigates the type of assurances that are needed in the contesting process when algorithmic black-boxes are involved, opening new questions about the interplay of contestability and explainability. We argue that specialised complementary methodologies to evaluate automated decision-making in the case of a particular decision being contested need to be developed. Further, we propose a combination of well-established software engineering and rule-based approaches as a possible socio-technical solution to the issue of contestability, one of the new democratic challenges posed by the automation of decision making.