The philosophical basis of algorithmic recourse

By Suresh Venkatasubramanian, Mark Alfano. 2020

155 citations recorded by the source corpus.

Philosophers have established that certain ethically important values are modally robust in the sense that they systematically deliver correlative benefits across a range of counterfactual scenarios. In this paper, we contend that recourse - the systematic process of reversing unfavorable decisions by algorithms and bureaucracies across a range of counterfactual scenarios - is such a modally robust good. In particular, we argue that two essential components of a good life - temporally extended agency and trust - are underwritten by recourse. We critique existing approaches to the conceptualization, operationalization and implementation of recourse. Based on these criticisms, we suggest a revised approach to recourse and give examples of how it might be implemented - especially for those who are least well off1.

Subfields: Artificial Intelligence, Safety Research

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Citations in the corpus

On the Fairness of Causal Algorithmic Recourse (2020, background): “…(Joshi et al. 2019; Ustun, Spangher, and Liu 2019; Sharma, Henderson, and Ghosh 2019; Mahajan, Tan, and Sharma 2019; Mothilal, Sharma, and Tan 2020; Venkatasubramanian and Alfano 2020; Karimi et al. 2020c,b; Karimi, Schölkopf, and Valera 2021; Upadhyay, Joshi, and Copyright © 2022, Association…”
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020, background): “While providing some insight (explanation) into the underlying black-box classifier, such counterfactual explanations do not directly translate into actionable recommendations to individuals for obtaining a more favourable prediction[22, 5]—a related task referred to as algorithmic recourse [54, 55,”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “person to obtain a desired outcome from a fixed model” [171]; or “the systematic process of reversing unfavorable decisions by algorithms and bureaucracies across a range of counterfactual scenarios” [174]. We submit that recourse can be achieved by an affected data-subject if they can understand [5”
Algorithmic Recourse: from Counterfactual Explanations to Interventions (2020, background): “riables are deemed endogenous vs exogenous. Our formulation optimizes for interventions on endogenous variables that are directly actionable by the individual seeking recourse or an assigned ﬁduciary [46]. This assumption, can be extended to consider changes to exogenous variables via actions of a c”
The hidden assumptions behind counterfactual explanations and principal reasons (2019, background): “In work concurrent with our own, Venkatasubramanian and Alfano raise similar concerns and offer some potential solutions [45].”
Problems with Shapley-value-based explanations as feature importance measures (2020, methodology): “This setup has been formalized as the “counterfactual explanation” problem by Wachter et al. (2017) (with an analysis of hidden assumptions by Barocas et al.”
Towards Robust and Reliable Algorithmic Recourse (2021, result): “Similar arguments have been echoed in several other recent works [28, 13, 20].”