Leveraging Argumentation for Generating Robust Sample-based Explanations

Amgoud, Leila; Muller, Philippe; Trenquier, Henri

doi:10.24963/ijcai.2023/346

Leveraging Argumentation for Generating Robust Sample-based Explanations

Leila Amgoud, Philippe Muller, Henri Trenquier

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Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. Main Track. Pages 3104-3111.

https://doi.org/10.24963/ijcai.2023/346

Keywords

AI Ethics, Trust, Fairness: ETF: Explainability and interpretability
Knowledge Representation and Reasoning: KRR: Argumentation

Abstract

Explaining predictions made by inductive classifiers has become crucial with the rise of complex models acting more and more as black-boxes.

Abductive explanations are one of the most popular types of explanations that are provided for the purpose. They highlight feature-values that

are sufficient for making predictions. In the literature, they are generated by exploring the whole feature space, which is unreasonable in practice.

This paper solves the problem by introducing explanation functions that generate abductive explanations from a sample of instances. It shows

that such functions should be defined with great care since they cannot satisfy two desirable properties at the same time, namely existence of

explanations for every individual decision (success) and correctness of explanations (coherence). The paper provides a parameterized family of

argumentation-based explanation functions, each of which satisfies one of the two properties. It studies their formal properties and their experimental

behaviour on different datasets.

Leila Amgoud, Philippe Muller, and Henri Trenquier. 2023. Leveraging Argumentation for Generating Robust Sample-based Explanations. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23. IJCAI, 3104–3111. https://doi.org/10.24963/ijcai.2023/346

@inproceedings{ijcai2023p346,
  title     = {Leveraging Argumentation for Generating Robust Sample-based Explanations},
  author    = {Amgoud, Leila and Muller, Philippe and Trenquier, Henri},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {3104--3111},
  year      = {2023},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2023/346},
  url       = {https://doi.org/10.24963/ijcai.2023/346},
}

TY  - CPAPER
TI  - Leveraging Argumentation for Generating Robust Sample-based Explanations
AU  - Amgoud, Leila
AU  - Muller, Philippe
AU  - Trenquier, Henri
T2  - Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2023
SP  - 3104
EP  - 3111
DO  - 10.24963/ijcai.2023/346
UR  - https://doi.org/10.24963/ijcai.2023/346
ER  -