Machine Learning for Criminology and Crime Research At the Crossroads

Machine Learning for Criminology and Crime Research At the Crossroads

Machine Learning for Criminology and Crime Research: At the Crossroads reviews the roots of the intersection between machine learning, artificial intelligence (AI), and research on crime; examines the current state of the art in this area of scholarly inquiry; and discusses future perspectives that m......
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<EM>Machine Learning for Criminology and Crime Research: At the Crossroads</EM> reviews the roots of the intersection between machine learning, artificial intelligence (AI), and research on crime; examines the current state of the art in this area of scholarly inquiry; and discusses future perspectives that may emerge from this relationship. <P>As machine learning and AI approaches become increasingly pervasive, it is critical for criminology and crime research to reflect on the ways in which these paradigms could reshape the study of crime. In response, this book seeks to stimulate this discussion. The opening part is framed through a historical lens, with the first chapter dedicated to the origins of the relationship between AI and research on crime, refuting the "novelty narrative" that often surrounds this debate. The second presents a compact overview of the history of AI, further providing a nontechnical primer on machine learning. The following chapter reviews some of the m
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Machine Learning for Criminology and Crime Research: At the Crossroadsreviews the roots of the intersection between machine learning, artificial intelligence (AI), and research on crime; examines the current state of the art in this area of scholarly inquiry; and discusses future perspectives that may emerge from this relationship. As machine learning and AI approaches become increasingly pervasive, it is critical for criminology and crime research to reflect on the ways in which these paradigms could reshape the study of crime. In response, this book seeks to stimulate this discussion. The opening part is framed through a historical lens, with the first chapter dedicated to the origins of the relationship between AI and research on crime, refuting the "novelty narrative" that often surrounds this debate. The second presents a compact overview of the history of AI, further providing a nontechnical primer on machine learning. The following chapter reviews some of the most important trends in computational criminology and quantitatively characterizing publication patterns at the intersection of AI and criminology, through a network science approach. This book also looks to the future, proposing two goals and four pathways to increase the positive societal impact of algorithmic systems in research on crime. The sixth chapter provides a survey of the methods emerging from the integration of machine learning and causal inference, showcasing their promise for answering a range of critical questions.　With its transdisciplinary approach, Machine Learning for Criminology and Crime Research is important reading for scholars and students in criminology, criminal justice, sociology, and economics, as well as AI, data sciences and statistics, and computer science.

Produktinformasjon

Oppdag fremtiden med Machine Learning for Criminology and Crime Research

Machine Learning for Criminology and Crime Research: At the Crossroads er en banebrytende bok som utforsker skjæringspunktet mellom maskinlæring, kunstig intelligens (AI) og kriminalitetsforskning. Dette er ikke bare en akademisk tekst, men en viktig ressurs for alle som ønsker å forstå hvordan moderne teknologi kan revolusjonere vår forståelse av kriminalitet.

Bearbeidede temaer og strukturen i boken

  • Historisk perspektiv: Den første delen gir en grundig titt på historien til AI og dens innflytelse på kriminalitetsforskning.
  • Akkumulerte trender: Boken analyserer viktige trender innen beregningskriminologi og avdekker publikasjonsmønstre ved hjelp av nettverksvitenskap.
  • Fremtidsutsikter: Avslutningskapitlet skisserer to mål og fire veier for å maksimere den positive samfunnsmessige effekten av algoritmiske systemer.

Hvorfor velge denne boken?

Med sin tverrfaglige tilnærming er Machine Learning for Criminology and Crime Research et must-read for akademikere, studenter, og profesjonelle innen kriminologi, kriminaljustis, økonomi, samt IT- og datavitenskap. Den gir leseren verktøyene til å analysere og navigere i komplekse problemstillinger knyttet til kriminalitet ved hjelp av moderne datateknikker.

En book som inspirerer

Boken inneholder ikke bare teoretiske tilnærminger; den presenterer også praktiske metoder innen maskinlæring og kausal inferens, noe som gjør det mulig å adressere kritiske spørsmål i kriminalitetsforskning. Om du ønsker å være i forkant av utviklingen i dette spennende feltet, er dette boken for deg!

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