# Modelling GDPR-Compliant Explanations for Trustworthy AI

arXiv: https://arxiv.org/abs/2109.04165 · HTML: https://mlsanity.com/paper/2109.04165/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2021.

## Built on by (most cited first, 8 of 8)

- [An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability](https://mlsanity.com/paper/2109.05327/) (2021)
- [Making Things Explainable vs Explaining: Requirements and Challenges under the GDPR](https://mlsanity.com/paper/2110.00758/) (2021)
- [Assessing Gender Bias in Predictive Algorithms using eXplainable AI](https://mlsanity.com/paper/2203.10264/) (2022)
- [Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education](https://mlsanity.com/paper/2402.15027/) (2024)
- [Aligning XAI with EU Regulations for Smart Biomedical Devices: A Methodology for Compliance Analysis](https://mlsanity.com/paper/2408.15121/) (2024)
- [Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements](https://mlsanity.com/paper/2604.09628/) (2026)
- [Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces](https://mlsanity.com/paper/2110.00762/) (2021)
- [A Fine-Grained Complexity View on Propositional Abduction -- Algorithms and Lower Bounds](https://mlsanity.com/paper/2505.10201/) (2025)

## Abstract (arXiv)

Through the General Data Protection Regulation (GDPR), the European Union has set out its vision for Automated Decision- Making (ADM) and AI, which must be reliable and human-centred. In particular we are interested on the Right to Explanation, that requires industry to produce explanations of ADM. The High-Level Expert Group on Artificial Intelligence (AI-HLEG), set up to support the implementation of this vision, has produced guidelines discussing the types of explanations that are appropriate for user-centred (interactive) Explanatory Tools. In this paper we propose our version of Explanatory Narratives (EN), based on user-centred concepts drawn from ISO 9241, as a model for user-centred explanations aligned with the GDPR and the AI-HLEG guidelines. Through the use of ENs we convert the problem of generating explanations for ADM 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. To this end we list suitable exploration heuristics, we study the properties and structure of explanations, and discuss the proposed model identifying its weaknesses and strengths.