@inproceedings{Kori:2025,
author = {Kori, A and Rago, A and Toni, F},
publisher = {ACM},
title = {Free argumentative exchanges for explaining image classifiers},
year = {2025}
}

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TY  - CPAPER
AB  - Deep learning models are powerful image classifiers but their opacity hinders their trustworthiness. Explanation methods for capturing the reasoning process within these classifiers faithfully and in a cognitively manageable manner are scarce, due to their sheer complexity and size. In this paper, we provide a solution for this problem by defining a novel method for explaining the outputs of image classifiers with debates between two agents, each arguing for a particular class. We obtain these debates as concrete instances of Free Argumentative eXchanges (FAXs), a novel argumentation-based multi-agent framework allowing agents to internalise opinions by other agents differently than originally stated. We define two metrics to assess the usefulness of FAXs as argumentative explanationsfor image classifiers. We then conduct a number of empirical experiments showing that FAXs perform well along these metrics as well as being more faithful to the image classifiers than conventional, non-argumentative explanation methods.
AU  - Kori,A
AU  - Rago,A
AU  - Toni,F
PB  - ACM
PY  - 2025///
TI  - Free argumentative exchanges for explaining image classifiers
ER  - 

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