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High-stakes team based public sector decision making and AI oversight

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  • Morgan, Deborah
  • Hashem, Youmna
  • Straub, Vincent John
  • Bright, Jonathan

Abstract

Oversight mechanisms, whereby the functioning and behaviour of AI systems are controlled to ensure that they are tuned to public benefit, are a core aspect of human-centered AI. They are especially important in public sector AI applications, where decisions on core public services such as education, benefits, and child welfare have significant impacts. Much current thinking on oversight mechanisms revolves around the idea of human decision makers being present ‘in the loop’ of decision making, such that they can insert expert judgment at critical moments and thus rein in the functioning of the machine. While welcome, we believe that the theory of human in the loop oversight has yet to fully engage with the idea that decision making, especially in high-stakes contexts, is often currently made by hierarchical teams rather than one individual. This raises the question of how such hierarchical structures can effectively engage with an AI system that is either supporting or making decisions. In this position paper, we outline some of the key contemporary elements of hierarchical decision making in contemporary public services and show how they relate to current thinking about AI oversight, thus sketching out future research directions for the field.

Suggested Citation

  • Morgan, Deborah & Hashem, Youmna & Straub, Vincent John & Bright, Jonathan, 2022. "High-stakes team based public sector decision making and AI oversight," SocArXiv arq3w, Center for Open Science.
    • Handle: RePEc:osf:socarx:arq3w
    DOI: 10.31219/osf.io/arq3w
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    References listed on IDEAS

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    1. Magdalena Dobrajska & Stephan Billinger & Samina Karim, 2015. "Delegation Within Hierarchies: How Information Processing and Knowledge Characteristics Influence the Allocation of Formal and Real Decision Authority," Organization Science, INFORMS, vol. 26(3), pages 687-704, June.
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