| Title: |
Arguments based on domain rules in prediction justifications |
| Authors: |
Peters, Joeri; Bex, Floris; Prakken, Henry; Sub Responsible AI; Responsible AI |
| Publication Year: |
2024 |
| Subject Terms: |
Case-Based Argumentation; Domain Knowledge; Explainable AI; Precedential Constraint; General Computer Science; SDG 16 - Peace; Justice and Strong Institutions |
| Description: |
Ensuring the interpretability of trained machine learning models is often paramount, particularly in high-stakes domains such as counter-terrorism and other forms of law enforcement. Post hoc techniques have emerged as a promising avenue for justifying the predictions of complex models. However, while these approaches provide valuable insights, they often lack the ability to directly reference familiar domain rules and make use of these rules to guide explanations. This paper introduces a method for incorporating arguments about the applicability of domain rules in justifying classifier predictions |
| Document Type: |
book part |
| File Description: |
application/pdf |
| Language: |
English |
| ISSN: |
1613-0073 |
| Relation: |
https://dspace.library.uu.nl/handle/1874/482351 |
| Availability: |
https://dspace.library.uu.nl/handle/1874/482351 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.AB0695A2 |
| Database: |
BASE |