# Human-centred explanation of rule-based decision-making systems in the legal domain

arXiv: https://arxiv.org/abs/2310.16704 · HTML: https://mlsanity.com/paper/2310.16704/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)

## Abstract (arXiv)

We propose a human-centred explanation method for rule-based automated decision-making systems in the legal domain. Firstly, we establish a conceptual framework for developing explanation methods, representing its key internal components (content, communication and adaptation) and external dependencies (decision-making system, human recipient and domain). Secondly, we propose an explanation method that uses a graph database to enable question-driven explanations and multimedia display. This way, we can tailor the explanation to the user. Finally, we show how our conceptual framework is applicable to a real-world scenario at the Dutch Tax and Customs Administration and implement our explanation method for this scenario.