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Oversight is rightly recognised as vital within high-stakes public sector AIapplications, where decisions can have profound individual and collectiveimpacts. Much current thinking regarding forms of oversight mechanisms for AIwithin the public sector revolves around the idea of human decision makersbeing 'in-the-loop' and thus being able to intervene to prevent errors andpotential harm. However, in a number of high-stakes public sector contexts,operational oversight of decisions is made by expert teams rather thanindividuals. The ways in which deployed AI systems can be integrated into theseexisting operational team oversight processes has yet to attract muchattention. We address this gap by exploring the impacts of AI upon pre-existingoversight of clinical decision-making through institutional analysis. We findthat existing oversight is nested within professional training requirements andrelies heavily upon explanation and questioning to elicit vital information.Professional bodies and liability mechanisms also act as additional levers ofoversight. These dimensions of oversight are impacted, and potentiallyreconfigured, by AI systems. We therefore suggest a broader lens of'team-in-the-loop' to conceptualise the system-level analysis required foradoption of AI within high-stakes public sector deployment.

'Team-in-the-loop' organisational oversight of high-stakes AI