The automation of consequential decisions through opaque machine learning models in societal domains impedes our agency. This paper is about how agency can be reinstated by the provision of certain kinds of knowledge. More precisely, we discuss whether a specific type of explanation, counterfactual explanations, facilitates our ability to contest algorithmic decisions. Against this backdrop, our paper makes three contributions: First, we develop an account of contestability, where contestability is defined as the provision of information, sufficient for a decision-subject to use as a basis for demanding that a decision be revoked. We also demarcate contestability from adjacent concepts in the discourse surrounding the right to explanation, such as justification and recourse. Second, we examine to what extent counterfactual explanations are conducive to contestability by considering a variety of failure modes causing problematic algorithmic decisions and scrutinize to what extent counterfactual explanations help us detect the underlying errors. Third, we propose ways in which, with certain modifications, counterfactual explanations can be made more fitting to serve the desired function. In this vein, we sketch the contours of a multi-shot approach to counterfactuals, where decision-subjects can query a model to test their own counterfactuals for a (limited) number of times.

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