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Use of Artificial Intelligence to support cybercrime research

Use of Artificial Intelligence to support cybercrime research
Use of Artificial Intelligence to support cybercrime research
Efficient processing of the ever-growing volume of cybercrime related posts, images and videos appearing online is a significant challenge. This chapter discusses the trend for criminology researchers to use artificial intelligence (AI) algorithms for relevance pre-filtering and quantitative analysis of content at scales larger than can be achieved working manually. Algorithms can be used as black box AI tools, or integrated into socio-technical AI methodologies where manual criminology analysis is used for content and observed behavior patterns that are hard for machines to understand. AI must be deployed with care however, balancing the advantages in terms of scaling up the criminology analysis with the possibility of introducing new sources of algorithmic bias and error. Two concrete case studies are presented describing socio-technical AI approaches.
Artificial intelligence, Machine learning, Natural Language Processing, Information Extraction, Socio-technical, facial recognition software, Object detection, Event Detection, Social Network Analysis, Predictive Policing
213-232
Palgarave Macmillan
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Lavorgna, Anita
Holt, Thomas
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Lavorgna, Anita
Holt, Thomas

Middleton, Stuart (2021) Use of Artificial Intelligence to support cybercrime research. In, Lavorgna, Anita and Holt, Thomas (eds.) Researching Cybercrimes: Methodologies, Ethics, and Critical Approaches. 1 ed. Palgarave Macmillan, pp. 213-232. (doi:10.1007/978-3-030-74837-1).

Record type: Book Section

Abstract

Efficient processing of the ever-growing volume of cybercrime related posts, images and videos appearing online is a significant challenge. This chapter discusses the trend for criminology researchers to use artificial intelligence (AI) algorithms for relevance pre-filtering and quantitative analysis of content at scales larger than can be achieved working manually. Algorithms can be used as black box AI tools, or integrated into socio-technical AI methodologies where manual criminology analysis is used for content and observed behavior patterns that are hard for machines to understand. AI must be deployed with care however, balancing the advantages in terms of scaling up the criminology analysis with the possibility of introducing new sources of algorithmic bias and error. Two concrete case studies are presented describing socio-technical AI approaches.

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More information

Published date: 1 July 2021
Keywords: Artificial intelligence, Machine learning, Natural Language Processing, Information Extraction, Socio-technical, facial recognition software, Object detection, Event Detection, Social Network Analysis, Predictive Policing

Identifiers

Local EPrints ID: 452593
URI: http://eprints.soton.ac.uk/id/eprint/452593
PURE UUID: a80a801d-4c99-42c8-b78a-1e87b56c8a8f
ORCID for Stuart Middleton: ORCID iD orcid.org/0000-0001-8305-8176

Catalogue record

Date deposited: 11 Dec 2021 11:28
Last modified: 23 Jul 2022 01:47

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Contributors

Editor: Anita Lavorgna
Editor: Thomas Holt

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