As AI regulations around the world intensify their focus on system safety, contestability has become a mandatory, yet ill-defined, safeguard. In XAI, "contestability" remains an empty promise: no formal definition exists, no algorithm guarantees it, and practitioners lack concrete guidance to satisfy regulatory requirements. Grounded in a systematic literature review, this paper presents the first rigorous formal definition of contestability in explainable AI, directly aligned with stakeholder requirements and regulatory mandates. We introduce a modular framework of by-design and post-hoc mechanisms spanning human-centered interfaces, technical architectures, legal processes, and organizational workflows. To operationalize our framework, we propose the Contestability Assessment Scale, a composite metric built on more than twenty quantitative criteria. Through multiple case studies across diverse application domains, we reveal where state-of-the-art systems fall short and show how our framework drives targeted improvements. By converting contestability from regulatory theory into a practical framework, our work equips practitioners with the tools to embed genuine recourse and accountability into AI systems.
View on arXiv@article{moreira2025_2506.01662,
title={ Explainable AI Systems Must Be Contestable: Here's How to Make It Happen },
author={ Catarina Moreira and Anna Palatkina and Dacia Braca and Dylan M. Walsh and Peter J. Leihn and Fang Chen and Nina C. Hubig },
journal={arXiv preprint arXiv:2506.01662},
year={ 2025 }
}