The University of Southampton
University of Southampton Institutional Repository

Countering plant crime online: cross-disciplinary collaboration in the FloraGuard study

Countering plant crime online: cross-disciplinary collaboration in the FloraGuard study
Countering plant crime online: cross-disciplinary collaboration in the FloraGuard study
The illegal online trade in plants has potentially devastating impacts upon species poached for sale in digital markets, yet the scale of this threat to endangered species of flora remains relatively undetermined. Effectively monitoring and analysing the online trade in plants, requires an efficient means of searching the vastness of cyberspace, and the expertise to differentiate legal from potentially illegal wildlife trade (IWT). Artificial Intelligence (AI) offers a means of improving the efficiency of both search and analysis techniques, although the complexities of wildlife trade, and the need to monitor thousands of different species, makes the automation of this technology extremely challenging. In this contribution, we review a novel socio-technical approach to addressing this problem. Combining expertise in information and communications technology, criminology, law enforcement and conservation science, this cross-disciplinary technique combines AI algorithms with human judgement and expertise, to search for and iteratively analyse potentially relevant online content. We suggest that by coupling the scalability of search algorithms with a sufficient level of human input required to evaluate wildlife trade data, the proposed methodological approach offers significant advantages over manual search techniques. We conclude by examining the high level of cross-disciplinary collaboration required to develop this technique, which may provide a useful case study for conservation practitioners and law enforcement agencies, seeking to tackle this technology-driven threat to biodiversity.
Artificial intelligence, CITES, Cross-disciplinary, Cybercrime, Natural Language Processing, Illegal Wildlife Trade
Whitehead, David
baf6a255-0682-4a9c-af25-3eab6929c43c
Cowell, Carly
8400730f-2332-461c-9a05-47b3f976f4ed
Lavorgna, Anita
6e34317e-2dda-42b9-8244-14747695598c
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Whitehead, David
baf6a255-0682-4a9c-af25-3eab6929c43c
Cowell, Carly
8400730f-2332-461c-9a05-47b3f976f4ed
Lavorgna, Anita
6e34317e-2dda-42b9-8244-14747695598c
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f

Whitehead, David, Cowell, Carly, Lavorgna, Anita and Middleton, Stuart (2021) Countering plant crime online: cross-disciplinary collaboration in the FloraGuard study. Forensic Science International: Animals and Environments, 1, [100007]. (doi:10.1016/j.fsiae.2021.100007).

Record type: Article

Abstract

The illegal online trade in plants has potentially devastating impacts upon species poached for sale in digital markets, yet the scale of this threat to endangered species of flora remains relatively undetermined. Effectively monitoring and analysing the online trade in plants, requires an efficient means of searching the vastness of cyberspace, and the expertise to differentiate legal from potentially illegal wildlife trade (IWT). Artificial Intelligence (AI) offers a means of improving the efficiency of both search and analysis techniques, although the complexities of wildlife trade, and the need to monitor thousands of different species, makes the automation of this technology extremely challenging. In this contribution, we review a novel socio-technical approach to addressing this problem. Combining expertise in information and communications technology, criminology, law enforcement and conservation science, this cross-disciplinary technique combines AI algorithms with human judgement and expertise, to search for and iteratively analyse potentially relevant online content. We suggest that by coupling the scalability of search algorithms with a sufficient level of human input required to evaluate wildlife trade data, the proposed methodological approach offers significant advantages over manual search techniques. We conclude by examining the high level of cross-disciplinary collaboration required to develop this technique, which may provide a useful case study for conservation practitioners and law enforcement agencies, seeking to tackle this technology-driven threat to biodiversity.

Text
pagination_FSIAE_100007 - Proof
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 15 April 2021
e-pub ahead of print date: 1 May 2021
Published date: November 2021
Additional Information: Articve is gold open access (see link to PDF)
Keywords: Artificial intelligence, CITES, Cross-disciplinary, Cybercrime, Natural Language Processing, Illegal Wildlife Trade

Identifiers

Local EPrints ID: 452998
URI: http://eprints.soton.ac.uk/id/eprint/452998
PURE UUID: 5fed9008-e469-45d9-95c1-79e8a23d23b8
ORCID for Anita Lavorgna: ORCID iD orcid.org/0000-0001-8484-1613
ORCID for Stuart Middleton: ORCID iD orcid.org/0000-0001-8305-8176

Catalogue record

Date deposited: 07 Jan 2022 12:12
Last modified: 23 Jul 2022 02:11

Export record

Altmetrics

Contributors

Author: David Whitehead
Author: Carly Cowell
Author: Anita Lavorgna ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×