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Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach
[article]
2021
arXiv
pre-print
We examine counterfactual explanations for explaining the decisions made by model-based AI systems. The counterfactual approach we consider defines an explanation as a set of the system's data inputs that causally drives the decision (i.e., changing the inputs in the set changes the decision) and is irreducible (i.e., changing any subset of the inputs does not change the decision). We (1) demonstrate how this framework may be used to provide explanations for decisions made by general,
arXiv:2001.07417v5
fatcat:76n6mxrnonca7myaujflzu7rdi