Abstract
In recent work, theories of case-based legal reasoning have been applied to the development of explainable artificial intelligence methods, through the analogy of training examples as previously decided cases. One such theory is that of precedential constraint. A downside of this theory with respect to this application is that it performs single-step reasoning, moving directly from the case base to an outcome. For this reason we propose a generalization of the theory of precedential constraint which allows multi-step reasoning, moving from the case base through a series of intermediate legal concepts before arriving at an outcome. Our generalization revolves around the notion of factor hierarchy, so we call this hierarchical precedential constraint. We present the theory, demonstrate its applicability to case-based legal reasoning, and perform a preliminary analysis of its theoretical properties.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law |
| Publisher | Association for Computing Machinery |
| Pages | 333-342 |
| Number of pages | 10 |
| ISBN (Electronic) | 9798400701979 |
| DOIs | |
| Publication status | Published - 2023 |
Bibliographical note
Publisher Copyright:© ICAIL 2023. All rights reserved.
Funding
This research was (partially) funded by the Hybrid Intelligence Center, a 10-year programme funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research, grant number 024.004.022.
| Funders | Funder number |
|---|---|
| Ministerie van onderwijs, cultuur en wetenschap | |
| Nederlandse Organisatie voor Wetenschappelijk Onderzoek | 024.004.022 |
Keywords
- case-based reasoning
- explainable artificial intelligence
- factor hierarchy
- factors
- precedential constraint