000			01594nam a22001457a 4500
999			_c523261 _d523261
008			230803b ||||| |||| 00| 0 eng d
100			_aSelten, F., Robeer, M. and Grimmelikhuijsen, S. _942236
245			_aJust like i thought’: Street-level bureaucrats trust AI recommendations if they confirm their professional judgment
260			_aPublic Administration Review
300			_a83(2), Mar-Apr, 2023: p.263-278
520			_aArtificial Intelligence is increasingly used to support and improve street-level decision-making, but empirical evidence on how street-level bureaucrats' work is affected by AI technologies is scarce. We investigate how AI recommendations affect street-level bureaucrats' decision-making and if explainable AI increases trust in such recommendations. We experimentally tested a realistic mock predictive policing system in a sample of Dutch police officers using a 2 × 2 factorial design. We found that police officers trust and follow AI recommendations that are congruent with their intuitive professional judgment. We found no effect of explanations on trust in AI recommendations. We conclude that police officers do not blindly trust AI technologies, but follow AI recommendations that confirm what they already thought. This highlights the potential of street-level discretion in correcting faulty AI recommendations on the one hand, but, on the other hand, poses serious limits to the hope that fair AI systems can correct human biases. – Reproduced
773			_aPublic Administration Review
906			_aARTIFICIAL INTELLIGENCE
942			_cAR