Afficher la notice abrégée

dc.contributor.authorRyberg, Jesper
dc.contributor.authorRoberts, Julian V.
dc.date.accessioned2025-03-07T21:28:41Z
dc.date.available2025-03-07T21:28:41Z
dc.date.issued2022
dc.date.submitted2024-05-23T11:49:01Z
dc.identifierOCN: 1313945072
dc.identifierhttps://library.oapen.org/handle/20.500.12657/90554
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/167501
dc.description.abstractThe 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.
dc.languageEnglish
dc.relation.ispartofseriesStudies in Penal Theory and Philosophy
dc.rightsopen access
dc.subject.classificationthema EDItEUR::L Law::LN Laws of specific jurisdictions and specific areas of law::LNF Criminal law: procedure and offences::LNFB Criminal justice law
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JK Social services and welfare, criminology::JKV Crime and criminology
dc.subject.classificationthema EDItEUR::L Law
dc.subject.otherArtificial Intelligence; AI; criminal sentencing; legal punishment; criminal justice; law
dc.titleSentencing and Artificial Intelligence
dc.typebook
oapen.relation.isPublishedBydb4e319f-ca9f-449a-bcf2-37d7c6f885b1
oapen.relation.hasChapterChapter 6 Learning to Discriminate
oapen.relation.isbn9780197539538
oapen.pages296


Fichier(s) constituant ce document

FichiersTailleFormatVue

Il n'y a pas de fichiers associés à ce document.

Ce document figure dans la(les) collection(s) suivante(s)

Afficher la notice abrégée

Chapters in this book

  • Davies, Benjamin; Douglas, Thomas (2022)
    It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. ...