E. Tan, Maxime Petit Jean, Anthony Simonofski, Thomas Tombal, Bjorn Kleizen, M. Sabbe, Lucas Bechoux, Pauline Willem
{"title":"Artificial intelligence and algorithmic decisions in fraud detection: An interpretive structural model","authors":"E. Tan, Maxime Petit Jean, Anthony Simonofski, Thomas Tombal, Bjorn Kleizen, M. Sabbe, Lucas Bechoux, Pauline Willem","doi":"10.1017/dap.2023.22","DOIUrl":null,"url":null,"abstract":"Abstract The use of artificial intelligence and algorithmic decision-making in public policy processes is influenced by a range of diverse drivers. This article provides a comprehensive view of 13 drivers and their interrelationships, identified through empirical findings from the taxation and social security domains in Belgium. These drivers are organized into five hierarchical layers that policy designers need to focus on when introducing advanced analytics in fraud detection: (a) trust layer, (b) interoperability layer, (c) perceived benefits layer, (d) data governance layer, and (e) digital governance layer. The layered approach enables a holistic view of assessing adoption challenges concerning new digital technologies. The research uses thematic analysis and interpretive structural modeling.","PeriodicalId":93427,"journal":{"name":"Data & policy","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data & policy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/dap.2023.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PUBLIC ADMINISTRATION","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract The use of artificial intelligence and algorithmic decision-making in public policy processes is influenced by a range of diverse drivers. This article provides a comprehensive view of 13 drivers and their interrelationships, identified through empirical findings from the taxation and social security domains in Belgium. These drivers are organized into five hierarchical layers that policy designers need to focus on when introducing advanced analytics in fraud detection: (a) trust layer, (b) interoperability layer, (c) perceived benefits layer, (d) data governance layer, and (e) digital governance layer. The layered approach enables a holistic view of assessing adoption challenges concerning new digital technologies. The research uses thematic analysis and interpretive structural modeling.
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