| 000 -LEADER |
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| fixed length control field |
01594nam a22001457a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230803b ||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Selten, F., Robeer, M. and Grimmelikhuijsen, S. |
| 245 ## - TITLE STATEMENT |
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| Title |
Just like i thought’: Street-level bureaucrats trust AI recommendations if they confirm their professional judgment |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Public Administration Review |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
83(2), Mar-Apr, 2023: p.263-278 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Artificial 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 ## - HOST ITEM ENTRY |
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| Main entry heading |
Public Administration Review |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
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| Subject DIP |
ARTIFICIAL INTELLIGENCE |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Item type |
Articles |
