Abstract
In recent years, a model of a fortiori argumentation, developed to describe legal reasoning based on precedent, has been successfully applied in the field of artificial intelligence to improve interpretability of data-driven decision systems. In order to make this model more broadly applicable for this purpose, work has been done to expand the knowledge representation on the basis of which it functions, as the original model accommodates only binary propositional information. In particular, two separate expansions of the original model emerged; one which accounts for non-binary input information, and a second which accommodates hierarchically structured reasoning. In the present work we unify these expansions to a single model, incorporating both dimensional and hierarchical information.
| Original language | English |
|---|---|
| Title of host publication | Legal Knowledge and Information Systems |
| Subtitle of host publication | 36th Annual Conference |
| Editors | Giovanni Sileno, Jerry Spanakis, Gijs van Dijck |
| Publisher | IOS Press |
| Pages | 43-52 |
| Number of pages | 10 |
| ISBN (Electronic) | 978-1-64368-473-4 |
| ISBN (Print) | 978-1-64368-472-7 |
| DOIs | |
| Publication status | Published - 2023 |
Publication series
| Name | Frontiers in Artificial Intelligence and Applications |
|---|---|
| Volume | 379 |
| ISSN (Print) | 0922-6389 |
Bibliographical note
Publisher Copyright:© 2023 The Authors.
Keywords
- a fortiori reasoning
- dimensions
- explainable artificial intelligence
- hierarchy
- precedential constraint