Abstract
Post hoc analyses are used to provide interpretable explanations for machine learning predictions made
by an opaque model. We modify a top-level model (AF-CBA) that uses case-based argumentation as such
a post hoc analysis. AF-CBA justifies model predictions on the basis of an argument graph constructed
using precedents from a case base. The effectiveness of this approach is limited when faced with an
inconsistent case base, which are frequently encountered in practice. Reducing an inconsistent case base
to a consistent subset is possible but undesirable. By altering the approach’s definition of best precedent
to include an additional criterion based on an expression of authoritativeness, we allow AF-CBA to
handle inconsistent case bases. We experiment with four different expressions of authoritativeness using
three different data sets in order to evaluate their effect on the explanations generated in terms of the
average number of precedents and the number of inconsistent a fortiori forcing relations.
by an opaque model. We modify a top-level model (AF-CBA) that uses case-based argumentation as such
a post hoc analysis. AF-CBA justifies model predictions on the basis of an argument graph constructed
using precedents from a case base. The effectiveness of this approach is limited when faced with an
inconsistent case base, which are frequently encountered in practice. Reducing an inconsistent case base
to a consistent subset is possible but undesirable. By altering the approach’s definition of best precedent
to include an additional criterion based on an expression of authoritativeness, we allow AF-CBA to
handle inconsistent case bases. We experiment with four different expressions of authoritativeness using
three different data sets in order to evaluate their effect on the explanations generated in terms of the
average number of precedents and the number of inconsistent a fortiori forcing relations.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 1st International Workshop on Argumentation for eXplainable AI (ArgXAI 2022) co-located with 9th International Conference on Computational Models of Argument (COMMA 2022) Cardiff, Wales, September 12, 2022. |
| Editors | Kristijonas Čyras, Timotheus Kampik, Oana Cocarascu, Antonio Rago |
| Publisher | CEUR WS |
| Pages | 1-13 |
| Publication status | Published - 2022 |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Volume | 3209 |
| ISSN (Print) | 1613-0073 |
Keywords
- Justifications
- Inconsistent case bases
- Authoritativeness