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E-raamat: Machine Learning for Criminology and Crime Research: At the Crossroads [Taylor & Francis e-raamat]

  • Formaat: 176 pages, 7 Tables, black and white; 7 Line drawings, black and white; 7 Illustrations, black and white
  • Sari: Routledge Advances in Criminology
  • Ilmumisaeg: 10-Jun-2022
  • Kirjastus: Routledge
  • ISBN-13: 9781003217732
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 161,57 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 230,81 €
  • Säästad 30%
  • Formaat: 176 pages, 7 Tables, black and white; 7 Line drawings, black and white; 7 Illustrations, black and white
  • Sari: Routledge Advances in Criminology
  • Ilmumisaeg: 10-Jun-2022
  • Kirjastus: Routledge
  • ISBN-13: 9781003217732
Teised raamatud teemal:
"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 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"--

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.
List of Figures
xi
List of Tables
xii
Foreword xiii
Preface xvi
Acknowledgments xviii
1 The "Novelty Narrative": An Unorthodox Introduction
1(18)
The Mythical "Novelty Narrative"
1(2)
Being Novel before Being Novel
3(9)
This Book
12(3)
References
15(4)
2 A Collective Journey: A Short Overview on Artificial Intelligence
19(33)
Introduction
19(1)
A Long Journey - An Historical Account of AI
20(12)
Machine Learning or the Superstar of Our Times
32(11)
Conclusions
43(3)
References
46(6)
3 Criminology at the Crossroads? Computational Perspectives
52(41)
The Increasingly Computational Nature of Criminology
52(13)
Assessing the State of the Art in Research Intersecting Artificial Intelligence and Criminology
65(16)
Discussion and Conclusions
81(4)
References
85(8)
4 To Reframe and Reform: Increasing the Positive Social Impact of Algorithmic Applications in Research on Crime
93(34)
Introduction
93(2)
To Reframe: Recasting Computational Crime Research
95(10)
To Reform: Four Pathways
105(9)
Discussion and Conclusions
114(3)
References
117(10)
5 Causal Inference in Criminology and Crime Research and the Promises of Machine Learning
127(41)
Introduction
127(1)
Criminology's Quest for Causality
128(11)
Machine Learning: A Culture of Prediction?
139(7)
Research on Crime and the Contribution of AI in Estimating Causal Effects
146(11)
Conclusions
157(2)
References
159(9)
6 Concluding Remarks
168(1)
Final Notes 168(3)
Index 171
Gian Maria Campedelli is a Postdoctoral Research Fellow in Computational Sociology and Criminology at the University of Trento, Italy. In 2020, he earned a PhD in Criminology from Catholic University in Milan, Italy. From 2016 to 2019 he worked as a researcher at Transcrime, the Joint Research Center on Transnational Crime of Catholic University, University of Bologna, and University of Perugia. In 2018 he was also a visiting research scholar in the School of Computer Science at Carnegie Mellon University, in Pittsburgh, the United States. His research addresses the development and application of computational methods especially machine learning and complex networks to the study of criminal and social phenomena, with a specific focus on organized crime, violence, and terrorism.