{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T17:02:05Z","timestamp":1777741325190,"version":"3.51.4"},"reference-count":31,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IP"],"published-print":{"date-parts":[[2024,8,27]]},"abstract":"<jats:p>Algorithms are rapidly transforming government bureaucracies. The implications of this transformation for the work of public service employees are not yet well understood. So far, the literature has mostly neglected the use of algorithms by these \u201cscreen-level bureaucrats\u201d, and this constitutes a major gap in our knowledge about how algorithms affect bureaucracies. To understand the work of screen-level bureaucrats and to explore how they actively engage with algorithmic software to support their assessment of online fraud, we analyzed 45 hours of observations and 12 in-depth interviews at the Netherlands Police. We employ a socio-material perspective to analyze the dynamics between screen-level bureaucrats and algorithms. We conclude that for administrative tasks, algorithms help screen-level bureaucrats to perform their work by providing structured data and allowing them to focus more on assessments which need a nuanced judgement. At the same time, algorithmic advice in a decision-making task is simply ignored by the screen-level bureaucrats as they predominantly rely on their professional judgement in the assessment of online fraud reports. This highlights the need to further investigate how an algorithm should not only provide accurate advice to the screen-level bureaucrats but also convince them to follow it.<\/jats:p>","DOI":"10.3233\/ip-220070","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T12:20:36Z","timestamp":1702988436000},"page":"277-292","source":"Crossref","is-referenced-by-count":3,"title":["Screen-level bureaucrats in the age of algorithms: An ethnographic study of algorithmically supported public service workers in the Netherlands 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