Behavioural public administration meets data science: A behavioural research agenda on algorithmic decision-making

Abstract

Our contribution aims to propose a novel research agenda for behavioural public administration (BPA) regarding one of the most important developments in the public sector nowadays: the incorporation of artificial intelligence into public sector decision-making. We argue that this raises the prospect of distinct set of biases and challenges for decision-makers and citizens, that arise in the human-algorithm interaction, and that thus far remain under- investigated in a bureaucratic context. While BPA scholars have focused on human biases and data scientists on ‘machine bias’, algorithmic decision-making arises at the intersection between the two. In light of the growing reliance on algorithmic systems in the public sector, fundamentally shaping the way governments make and implement policy decisions, and given the high-stakes nature of their application in these settings, it becomes pressing to remedy this oversight. We argue that behavioural public administration is well-positioned to contribute to critical aspects of this debate. Accordingly, we identify concrete avenues for future research, and develop theoretical propositions.