Beyond But-for Test: Counterfactual Explanation in Abstract Argumentation via Actual Causality (Extended Version)
arXiv:2606.31080v1 Announce Type: cross Abstract: Counterfactual explanation in abstract argumentation calls for an answer to the what-if query: would the topic argument still be accepted if the status of certain other arguments were changed? Existing approaches are limited to the but-for test and fail to accommodate more refined counterfactual conditions. To overcome these limitations, we introduce an intervention-based counterfactual reasoning framework in abstract argumentation. Our approach encodes the acceptance conditions of arguments as equations, then defines an intervention operator t
The continuous evolution of AI demands more sophisticated methods for understanding and explaining decision-making processes, moving beyond simpler correlational approaches.
Improving counterfactual reasoning in AI systems enhances transparency, interpretability, and trust, critical for deploying AI in sensitive applications and decision-making.
This research introduces a more robust framework for understanding 'what-if' scenarios in abstract argumentation, shifting from limited 'but-for' tests to intervention-based causal reasoning.
- · AI ethicists
- · Developers of explainable AI (XAI)
- · Users of complex AI systems
- · AI systems lacking transparency
More reliable and transparent AI decision-making processes can be developed.
Increased adoption of AI in high-stakes environments due to improved interpretability and accountability.
New regulatory frameworks for AI could incorporate requirements for sophisticated counterfactual explanation capabilities.
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Read at arXiv cs.AI