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The Use and Misuse of Counterfactuals in Ethical Machine Learning

Atoosa Kasirzadeh & Andrew Smart

In Atoosa Kasirzadeh & Andrew Smart (eds.), ACM Conference on Fairness, Accountability, and Transparency (FAccT 21) (2021)

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Counter Countermathematical Explanations.Atoosa Kasirzadeh - 2021 - Erkenntnis 88 (6):2537-2560.details Recently, there have been several attempts to generalize the counterfactual theory of causal explanations to mathematical explanations. The central idea of these attempts is to use conditionals whose antecedents express a mathematical impossibility. Such countermathematical conditionals are plugged into the explanatory scheme of the counterfactual theory and—so is the hope—capture mathematical explanations. Here, I dash the hope that countermathematical explanations simply parallel counterfactual explanations. In particular, I show that explanations based on countermathematicals are susceptible to three problems counterfactual explanations do (...) Download Export citation Bookmark 1 citation
Explainable AI and Causal Understanding: Counterfactual Approaches Considered.Sam Baron - 2023 - Minds and Machines 33 (2):347-377.details The counterfactual approach to explainable AI (XAI) seeks to provide understanding of AI systems through the provision of counterfactual explanations. In a recent systematic review, Chou et al. (Inform Fus 81:59–83, 2022) argue that the counterfactual approach does not clearly provide causal understanding. They diagnose the problem in terms of the underlying framework within which the counterfactual approach has been developed. To date, the counterfactual approach has not been developed in concert with the approach for specifying causes developed by Pearl (...) Download Export citation Bookmark 2 citations
From Explanation to Recommendation: Ethical Standards for Algorithmic Recourse.Emily Sullivan & Philippe Verreault-Julien - forthcoming - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES’22).details People are increasingly subject to algorithmic decisions, and it is generally agreed that end-users should be provided an explanation or rationale for these decisions. There are different purposes that explanations can have, such as increasing user trust in the system or allowing users to contest the decision. One specific purpose that is gaining more traction is algorithmic recourse. We first pro- pose that recourse should be viewed as a recommendation problem, not an explanation problem. Then, we argue that the capability (...) Download Export citation Bookmark

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