FACE: Feasible and Actionable Counterfactual Explanations
Open accessAbstract
Work in Counterfactual Explanations tends to focus on the principle of "the closest possible world" that identifies small changes leading to the desired outcome. In this paper we argue that while this approach might initially seem intuitively appealing it exhibits shortcomings not addressed in the current literature. First, a counterfactual example generated by the state-of-the-art systems is not necessarily representative of the underlying data distribution, and may therefore prescribe unachievable goals (e.g., an unsuccessful life insurance applicant with severe disability may be advised to do more sports). Secondly, the counterfactuals may not be based on a "feasible path" between the current state of the subject and the suggested one, making actionable recourse infeasible (e.g., low-skilled unsuccessful mortgage applicants may be told to double their salary, which may be hard without first increasing their skill level). These two shortcomings may render counterfactual explanations impractical and sometimes outright offensive. To address these two major flaws, first of all, we propose a new line of Counterfactual Explanations research aimed at providing actionable and feasible paths to transform a selected instance into one that meets a certain goal. Secondly, we propose FACE: an algorithmically sound way of uncovering these "feasible paths" based on the shortest path distances defined via density-weighted metrics. Our approach generates counterfactuals that are coherent with the underlying data distribution and supported by the "feasible paths" of change, which are achievable and can be tailored to the problem at hand.
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The present survey was undertaken as a systematic literature review following the guidelines by Kitchenham and Charters, Kitchenham et al., and Wohlin.
“The present survey has been undertaken as a systematic literature review following the guidelines by Kitchenham and Charters [58], Kitchenham et al. [59], and Wohlin [60].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [58]
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Cites
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A Survey on the Explainability of Supervised Machine Learning
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Method
- Authors
- Poyiadzi, R., Sokol, K., Santos-Rodriguez, R., De Bie, T., & Flach, P.
- Affiliations
- University of Bristol (GB)
- University of Bristol, Bristol, United Kingdom
- Ghent University, Ghent, Belgium
- Venue
- Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2020-02-07
- Pages
- 344-350
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W2973319951
- Cited by
- 317
- References
- 6
- Citations by year
- 2026: 44 · 2025: 70 · 2024: 62 · 2023: 59 · 2022: 50 · 2021: 28 · 2020: 4
- FWCI
- 13.47
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- EPSRC (EP/R005273/1)
- MRC (MC/PC/16029)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- counterfactual explanations, shortest path length, explainable AI, mortgage approval, life insurance, algorithmic fairness
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