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Artificial intelligence algorithms are increasingly adopted as decisionalaides by public bodies, with the promise of overcoming biases of humandecision-makers. At the same time, they may introduce new biases in thehuman-algorithm interaction. Drawing on psychology and public administrationliteratures, we investigate two key biases: overreliance on algorithmic adviceeven in the face of warning signals from other sources (automation bias), andselective adoption of algorithmic advice when this corresponds to stereotypes(selective adherence). We assess these via three experimental studies conductedin the NetherlandsWe discuss the implications of our findings for public sectordecision making in the age of automation. Overall, our study speaks topotential negative effects of automation of the administrative state foralready vulnerable and disadvantaged citizens.

Human-AI Interactions in Public Sector Decision-Making: "Automation Bias" and "Selective Adherence" to Algorithmic Advice