Counterfactual explanations and how to find them: literature review and benchmarking
By Riccardo Guidotti. Data mining and knowledge discovery, 2022
532 citations recorded by the source corpus.
Interpretable machine learning aims at unveiling the reasons behind predictions returned by uninterpretable classifiers. One of the most valuable types of explanation consists of counterfactuals. A counterfactual explanation reveals what should have been different in an instance to observe a diverse outcome. For instance, a bank customer asks for a loan that is rejected. The counterfactual explanation consists of what should have been different for the customer in order to have the loan accepted. Recently, there has been an explosion of proposals for counterfactual explainers. The aim of this work is to survey the most recent explainers returning counterfactual explanations. We categorize explainers based on the approach adopted to return the counterfactuals, and we label them according to characteristics of the method and properties of the counterfactuals returned. In addition, we visually compare the explanations, and we report quantitative benchmarking assessing minimality, actionability, stability, diversity, discriminative power, and running time. The results make evident that the current state of the art does not provide a counterfactual explainer able to guarantee all these properties simultaneously.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/22088210963a367ac66297ed4c945a9bfdf50afd
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In Artificial Intelligence
Citations in the corpus
- Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions (2023, methodology): “To address this issue, counterfactual generation methods for ML methods have garnered attention [32].”
- Benchmarking and survey of explanation methods for black box models (2021, unknown): “In (Guidotti 2022), counterfactual explanations are formalized as follows.”