# Making Things Explainable vs Explaining: Requirements and Challenges under the GDPR

arXiv: https://arxiv.org/abs/2110.00758 · HTML: https://mlsanity.com/paper/2110.00758/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 2 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017)
- [Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI](https://mlsanity.com/paper/1902.01876/) (2019)
- [Modelling GDPR-Compliant Explanations for Trustworthy AI](https://mlsanity.com/paper/2109.04165/) (2021)
- [From Philosophy to Interfaces: an Explanatory Method and a Tool Inspired by Achinstein's Theory of Explanation](https://mlsanity.com/paper/2109.04171/) (2021)

## Built on by (most cited first, 4 of 4)

- [Behaviour Trees for Creating Conversational Explanation Experiences](https://mlsanity.com/paper/2211.06402/) (2022)
- [Fiper: a Visual-based Explanation Combining Rules and Feature Importance](https://mlsanity.com/paper/2404.16903/) (2024)
- [Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanations](https://mlsanity.com/paper/2603.14894/) (2026)
- [Explaining Reinforcement Learning Decisions in Self-adaptive Systems](https://mlsanity.com/paper/2608.14620/) (2026)

## Abstract (arXiv)

The European Union (EU) through the High-Level Expert Group on Artificial Intelligence (AI-HLEG) and the General Data Protection Regulation (GDPR) has recently posed an interesting challenge to the eXplainable AI (XAI) community, by demanding a more user-centred approach to explain Automated Decision-Making systems (ADMs). Looking at the relevant literature, XAI is currently focused on producing explainable software and explanations that generally follow an approach we could term One-Size-Fits-All, that is unable to meet a requirement of centring on user needs. One of the causes of this limit is the belief that making things explainable alone is enough to have pragmatic explanations. Thus, insisting on a clear separation between explainabilty (something that can be explained) and explanations, we point to explanatorY AI (YAI) as an alternative and more powerful approach to win the AI-HLEG challenge. YAI builds over XAI with the goal to collect and organize explainable information, articulating it into something we called user-centred explanatory discourses. Through the use of explanatory discourses/narratives we represent the problem of generating explanations for Automated Decision-Making systems (ADMs) into the identification of an appropriate path over an explanatory space, allowing explainees to interactively explore it and produce the explanation best suited to their needs.