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The increasing use of predictive algorithms in consequential decision-making has made it imperative to carefully evaluate their impact and to structure appropriate regulatory responses. However, the standard approach to evaluating AI-assisted decisions has critical shortcomings, as it fails to account for the functional role of the algorithm as a tool for decision support and the dynamics of deployment. We argue that reliable evaluation of AI-assisted decisions requires broadening the unit of analysis from the algorithmic tool in abstraction to the socio-technological system in which the tool is embedded. Adopting this broader perspective, we propose four guidelines for evaluating AI decision support tools in a way that better accounts for the nature of the deployment of these systems, offering an agenda for a multi-disciplinary research programme.
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