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Machine Learning For Criminology And Criminal Research At The Crossroads 1st Edition Gian Maria Campedelli

  • SKU: BELL-48266362
Machine Learning For Criminology And Criminal Research At The Crossroads 1st Edition Gian Maria Campedelli
$ 31.00 $ 45.00 (-31%)

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Machine Learning For Criminology And Criminal Research At The Crossroads 1st Edition Gian Maria Campedelli instant download after payment.

Publisher: Routledge
File Extension: PDF
File size: 8.64 MB
Pages: 208
Author: Gian Maria Campedelli
ISBN: 9781032109190, 9781003217732, 9781032109282, 103210919X, 1003217737, 1032109289
Language: English
Year: 2022
Edition: 1

Product desciption

Machine Learning For Criminology And Criminal Research At The Crossroads 1st Edition Gian Maria Campedelli by Gian Maria Campedelli 9781032109190, 9781003217732, 9781032109282, 103210919X, 1003217737, 1032109289 instant download after payment.

Machine Learning for Criminology and Crime Research reviews the roots of the intersection between machine learning, Artificial Intelligence, 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 Artificial Intelligence (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 non-technical 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. The 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 final 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 an important reading for scholars and students in criminology, criminal justice, sociology and economics, as well as Artificial Intelligence, data sciences and statistics, and computer science.

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