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    Sentencing and Artificial Intelligence

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    Author(s)
    Ryberg, Jesper
    Roberts, Julian V.
    Language
    English
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    Abstract
    The first collective work devoted exclusively to the ethical and penal theoretical considerations of the use of artificial intelligence at sentencing Is it morally acceptable to use artificial intelligence (AI) in the determination of sentences on those who have broken the law? If so, how should such algorithms be used—and what are the consequences? Jesper Ryberg and Julian V. Roberts bring together leading experts to answer these questions. Sentencing and Artificial Intelligence investigates to what extent, and under which conditions, justice and the social good may be promoted by allocating parts of the most important task of the criminal court—that of determining legal punishment—to computerized sentencing algorithms. The introduction of an AI-based sentencing system could save significant resources and increase consistency across jurisdictions. But it could also reproduce historical biases, decrease transparency in decision-making, and undermine trust in the justice system. Dealing with a wide-range of pertinent issues including the transparency of algorithmic-based decision-making, the fairness and morality of algorithmic sentencing decisions, and potential discrimination as a result of these practices, this volume offers avaluable insight on the future of sentencing.
    URI
    https://library.oapen.org/handle/20.500.12657/90554
    Keywords
    Artificial Intelligence; AI; criminal sentencing; legal punishment; criminal justice; law
    ISBN
    9780197539538
    Publisher
    Oxford University Press
    Publisher website
    https://global.oup.com/
    Publication date and place
    2022
    Series
    Studies in Penal Theory and Philosophy,
    Classification
    Criminal justice law
    Crime and criminology
    Law
    Pages
    296
    Chapters in this book
    • Chapter 6 Learning to Discriminate
    Rights
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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