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• W3215206150 endingPage "2025" @default.
• W3215206150 startingPage "1999" @default.
• W3215206150 abstract "Efforts to develop autonomous and intelligent systems (AIS) have exploded across a range of settings in recent years, from self-driving cars to medical diagnostic chatbots. These have the potential to bring enormous benefits to society but also have the potential to introduce new-or amplify existing-risks. As these emerging technologies become more widespread, one of the most critical risk management challenges is to ensure that failures of AIS can be rigorously analyzed and understood so that the safety of these systems can be effectively governed and improved. AIS are necessarily developed and deployed within complex human, social, and organizational systems, but to date there has been little systematic examination of the sociotechnical sources of risk and failure in AIS. Accordingly, this article develops a conceptual framework that characterizes key sociotechnical sources of risk in AIS by reanalyzing one of the most publicly reported failures to date: the 2018 fatal crash of Uber's self-driving car. Publicly available investigative reports were systematically analyzed using constant comparative analysis to identify key sources and patterns of sociotechnical risk. Five fundamental domains of sociotechnical risk were conceptualized-structural, organizational, technological, epistemic, and cultural-each indicated by particular patterns of sociotechnical failure. The resulting SOTEC framework of sociotechnical risk in AIS extends existing theories of risk in complex systems and highlights important practical and theoretical implications for managing risk and developing infrastructures of learning in AIS." @default.
• W3215206150 created "2021-12-06" @default.
• W3215206150 creator A5033494708 @default.
• W3215206150 date "2021-11-23" @default.
• W3215206150 modified "2023-09-26" @default.
• W3215206150 title "Learning from the Failure of Autonomous and Intelligent Systems: Accidents, Safety, and Sociotechnical Sources of Risk" @default.
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• W3215206150 doi "https://doi.org/10.1111/risa.13850" @default.
• W3215206150 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34814229" @default.
• W3215206150 hasPublicationYear "2021" @default.

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