Even-Ifs From If-Onlys: Are the Best Semi-Factual Explanations Found Using Counterfactuals As Guides?
<|reference_start|>Even-Ifs From If-Onlys: Are the Best Semi-Factual Explanations Found Using Counterfactuals As Guides?: Recently, counterfactuals using "if-only" explanations have become very popular in eXplainable AI (XAI), as they describe which changes to feature-inputs of a black-box AI system result in changes to a (usually negative) decision-outcome. Even more recently, semi-factuals using "even-if" explanations have gained more attention. They elucidate the feature-input changes that do not change the decision-outcome of the AI system, with a potential to suggest more beneficial recourses. Some semi-factual methods use counterfactuals to the query-instance to guide semi-factual production (so-called counterfactual-guided methods), whereas others do not (so-called counterfactual-free methods). In this work, we perform comprehensive tests of 8 semi-factual methods on 7 datasets using 5 key metrics, to determine whether counterfactual guidance is necessary to find the best semi-factuals. The results of these tests suggests not, but rather that computing other aspects of the decision space lead to better semi-factual XAI.<|reference_end|>
@article{aryal2024even-ifs,
    title={Even-Ifs From If-Onlys: Are the Best Semi-Factual Explanations Found
  Using Counterfactuals As Guides?},
    author={Saugat Aryal, Mark T. Keane},
    journal={32nd International Conference on Case-Based Reasoning (ICCBR)
  2024, Merida, Mexico},
    year={2024},
    doi={10.1007/978-3-031-63646-2_3},
    archivePrefix={arXiv},
    eprint={2403.00980},
    primaryClass={cs.AI}
}