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Algorithms promise to predict delinquent behaviour, identify potentially dangerous persons, and support crime investigation. Algorithm-based applications are often deployed in this context, laying the groundwork for a \u2018smart criminal justice\u2019. In this qualitative study based on 32 interviews with criminal justice and police officials, we explore the reasons why and extent to which such a smart criminal justice system has already been established in Switzerland, and the benefits perceived by users. Drawing upon this research, we address the spread, application, technical background, institutional implementation, and psychological aspects of the use of algorithms in the criminal justice system. We find that the Swiss criminal justice system is already significantly shaped by algorithms, a change motivated by political expectations and demands for efficiency. Until now, algorithms have only been used at a low level of automation and technical complexity and the levels of benefit perceived vary. This study also identifies the need for critical evaluation and research-based optimization of the implementation of advanced technology. Societal implications, as well as the legal foundations of the use of algorithms, are often insufficiently taken into account. By discussing the main challenges to and issues with algorithm use in this field, this work lays the foundation for further research and debate regarding how to guarantee that \u2018smart\u2019 criminal justice is actually carried out smartly.<\/jats:p>","DOI":"10.1007\/s10506-022-09310-1","type":"journal-article","created":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T07:02:35Z","timestamp":1647241355000},"page":"213-237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Smart criminal justice: exploring the use of algorithms in the Swiss criminal justice system"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2644-8116","authenticated-orcid":false,"given":"Monika","family":"Simmler","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9563-7829","authenticated-orcid":false,"given":"Simone","family":"Brunner","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8868-5889","authenticated-orcid":false,"given":"Giulia","family":"Canova","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0580-692X","authenticated-orcid":false,"given":"Kuno","family":"Schedler","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,14]]},"reference":[{"issue":"6","key":"9310_CR1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1177\/0263276419851846","volume":"36","author":"L Amoore","year":"2019","unstructured":"Amoore L (2019) Doubt and the algorithm: on the partial accounts of machine learning. 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