000 -LEADER |
fixed length control field |
02771nam a22002057a 4500 |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
ISBN |
9781003217732 |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
364.072 |
Item number |
C193 2022 |
100 ## - MAIN ENTRY--AUTHOR NAME |
Personal name |
Campedelli, Gian Maria, author. |
245 ## - TITLE STATEMENT |
Title |
Machine learning for criminology and crime research : at the crossroads / Gian Maria Campedelli. |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
Place of publication |
New York : |
Name of publisher |
Routledge, Taylor & Francis Group, |
Year of publication |
©2022. |
300 ## - PHYSICAL DESCRIPTION |
Number of Pages |
1 online resource, xviii, 159 pages : |
Other physical details |
Illustrations. |
490 ## - SERIES STATEMENT |
Series statement |
(Routledge advances in criminology) |
500 ## - GENERAL NOTE |
General note |
Includes bibliographical references and index. |
505 ## - FORMATTED CONTENTS NOTE |
Formatted contents note |
Print version: Campedelli, Gian Maria. Machine learning for criminology and criminal research First Edition. London ; New York : Routledge, Taylor & Francis Group, 2022 9781032109190 (DLC) 2021059992 |
520 ## - SUMMARY, ETC. |
Summary, etc |
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"-- Provided by publisher. |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical Term |
Criminology--Research. |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical Term |
Artificial intelligence--Research. |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical Term |
Machine learning--Statistical methods. |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Koha item type |
Electronic Books |