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