FloraGuard: tackling the online illegal trade in endangered plants through a cross-disciplinary ICT-enabled methodology
FloraGuard: tackling the online illegal trade in endangered plants through a cross-disciplinary ICT-enabled methodology
This article presents a part of the ongoing Economic and Social Research Council (ESRC)-funded project “FloraGuard: Tackling the illegal trade in endangered plants” that relies on cross-disciplinary approaches to analyze online marketplaces for the illegal trade in endangered plants, and explores strategies to develop digital resources to assist law enforcement in countering and disrupting this criminal market. This contribution focuses on how the project brought together computer science, criminology, conservation science, and law enforcement expertise to create a tool for the automatic gathering of relevant online information to be used for research, intelligence, and investigative purposes. The article also discusses the ethical standards applied and proposes the concept of “artificial intelligence (AI) review” to provide a sociotechnical solution that builds trustworthiness in the AI approaches used for this type of cross-disciplinary information and communications technology (ICT)-enabled methodology.
ethics in online research, explainable AI, natural language processing, plant crimes, wildlife trafficking
428-450
Lavorgna, Anita
6e34317e-2dda-42b9-8244-14747695598c
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Pickering, Brian
225088d0-729e-4f17-afe2-1ad1193ccae6
Neumann, Geoffrey
9dfe6611-52bb-4ba6-ad83-b92c7acb4bb3
20 March 2020
Lavorgna, Anita
6e34317e-2dda-42b9-8244-14747695598c
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Pickering, Brian
225088d0-729e-4f17-afe2-1ad1193ccae6
Neumann, Geoffrey
9dfe6611-52bb-4ba6-ad83-b92c7acb4bb3
Lavorgna, Anita, Middleton, Stuart, Pickering, Brian and Neumann, Geoffrey
(2020)
FloraGuard: tackling the online illegal trade in endangered plants through a cross-disciplinary ICT-enabled methodology.
Journal of Contemporary Criminal Justice, 36 (3), .
(doi:10.1177/1043986220910297).
Abstract
This article presents a part of the ongoing Economic and Social Research Council (ESRC)-funded project “FloraGuard: Tackling the illegal trade in endangered plants” that relies on cross-disciplinary approaches to analyze online marketplaces for the illegal trade in endangered plants, and explores strategies to develop digital resources to assist law enforcement in countering and disrupting this criminal market. This contribution focuses on how the project brought together computer science, criminology, conservation science, and law enforcement expertise to create a tool for the automatic gathering of relevant online information to be used for research, intelligence, and investigative purposes. The article also discusses the ethical standards applied and proposes the concept of “artificial intelligence (AI) review” to provide a sociotechnical solution that builds trustworthiness in the AI approaches used for this type of cross-disciplinary information and communications technology (ICT)-enabled methodology.
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Accepted/In Press date: 20 January 2020
e-pub ahead of print date: 20 March 2020
Published date: 20 March 2020
Additional Information:
Funding Information:
We thank our research assistant Catherine Rutherford for her precious work in the preliminary stages of this research. We also thank Valentina Vaglica and Carly Cowell from the Royal Botanic Garden Kew for their continuous support, and Carly also for her comments on an earlier draft of this article. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Economic and Social Research Council [ES/R003254/1].
Publisher Copyright:
© The Author(s) 2020.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
Keywords:
ethics in online research, explainable AI, natural language processing, plant crimes, wildlife trafficking
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Local EPrints ID: 437583
URI: http://eprints.soton.ac.uk/id/eprint/437583
PURE UUID: b8de669a-9487-42bc-8c13-f5fd7e17c9b7
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Date deposited: 06 Feb 2020 17:30
Last modified: 17 Mar 2024 05:15
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Geoffrey Neumann
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