Explaining causal models with argumentation: the case of bi-variate reinforcement
File(s)
Accepted version
Author(s)
Rago, Antonio
Baroni, Pietro
Toni, Francesca
Type
Conference Paper
Abstract
Causal models are playing an increasingly important role in
machine learning, particularly in the realm of explainable AI.
We introduce a conceptualisation for generating argumenta-
tion frameworks (AFs) from causal models for the purpose
of forging explanations for the models’ outputs. The concep-
tualisation is based on reinterpreting desirable properties of
semantics of AFs as explanation moulds, which are means
for characterising the relations in the causal model argumen-
tatively. We demonstrate our methodology by reinterpreting
the property of bi-variate reinforcement as an explanation
mould to forge bipolar AFs as explanations for the outputs of
causal models. We perform a theoretical evaluation of these
argumentative explanations, examining whether they satisfy a
range of desirable explanatory and argumentative propertie
machine learning, particularly in the realm of explainable AI.
We introduce a conceptualisation for generating argumenta-
tion frameworks (AFs) from causal models for the purpose
of forging explanations for the models’ outputs. The concep-
tualisation is based on reinterpreting desirable properties of
semantics of AFs as explanation moulds, which are means
for characterising the relations in the causal model argumen-
tatively. We demonstrate our methodology by reinterpreting
the property of bi-variate reinforcement as an explanation
mould to forge bipolar AFs as explanations for the outputs of
causal models. We perform a theoretical evaluation of these
argumentative explanations, examining whether they satisfy a
range of desirable explanatory and argumentative propertie
Date Issued
2022-07-31
Date Acceptance
2022-04-15
Citation
Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, 2022, pp.505-509
ISSN
2334-1033
Publisher
IJCAI Organisation
Start Page
505
End Page
509
Journal / Book Title
Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning
Copyright Statement
Copyright © 2022 International Joint Conferences on Artificial Intelligence Organization
Identifier
https://proceedings.kr.org/2022/52/
Source
19th International Conference on Principles of Knowledge Representation and Reasoning (KR 2022)
Publication Status
Published
Start Date
2022-07-31
Finish Date
2022-08-05
Coverage Spatial
Haifa, Israel
Date Publish Online
2022-07-31