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The instrument we used is a machine learning version of RiskCanvi used by the Justice Department of <jats:italic>Catalonia, Spain<\/jats:italic>. It was hypothesized that people can improve their performance on defining the risk of recidivism when assisted with a RAI. Also, that professionals can perform better than non-experts on the domain. Participants had to predict whether a person who has been released from prison will commit a new crime leading to re-incarceration, within the next two years. This user study is done with (1) <jats:italic>general<\/jats:italic> participants from diverse backgrounds recruited through a crowdsourcing platform, (2) <jats:italic>targeted<\/jats:italic> participants who are students and practitioners of data science, criminology, or social work and professionals who work with RisCanvi. We also run focus groups with participants of the <jats:italic>targeted<\/jats:italic> study, including people who use <jats:italic>RisCanvi<\/jats:italic> in a professional capacity, to interpret the quantitative results. Among other findings, we observe that algorithmic support systematically leads to more accurate predictions from all participants, but that statistically significant gains are only seen in the performance of <jats:italic>targeted<\/jats:italic> participants with respect to that of crowdsourced participants. Among other comments, professional participants indicate that they would not foresee using a fully-automated system in criminal risk assessment, but do consider it valuable for training, standardization, and to fine-tune or double-check their predictions on particularly difficult cases. We found that the revised prediction by using a RAI increases the performance of all groups, while professionals show a better performance in general. And, a RAI can be considered for extending professional capacities and skills along their careers.<\/jats:p>","DOI":"10.1007\/s10506-024-09393-y","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T05:02:56Z","timestamp":1710478976000},"page":"471-517","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A comparative user study of human predictions in algorithm-supported recidivism risk assessment"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0255-0340","authenticated-orcid":false,"given":"Manuel","family":"Portela","sequence":"first","affiliation":[]},{"given":"Carlos","family":"Castillo","sequence":"additional","affiliation":[]},{"given":"Song\u00fcl","family":"Tolan","sequence":"additional","affiliation":[]},{"given":"Marzieh","family":"Karimi-Haghighi","sequence":"additional","affiliation":[]},{"given":"Antonio Andres","family":"Pueyo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"key":"9393_CR1","doi-asserted-by":"crossref","unstructured":"Andr\u00e9s-Pueyo A, Arbach-Lucioni K, Redondo S, Kroner J, Stephen Wormith SL, Desmarais Z (2018) The riscanvi: a new tool for assessing risk for violence in prison and recidivism. 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