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Bond, Alan 
ORCID: https://orcid.org/0000-0002-3809-5805, Cilliers, Dirk, Retief, Francois, Alberts, Reece, Roos, Claudine and Moolman, Jurie
(2024)
Using an Artificial Intelligence chatbot to critically review the scientific literature on the use of Artificial Intelligence in Environmental Impact Assessment.
Impact Assessment and Project Appraisal.
ISSN 1461-5517
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Abstract
There is considerable uncertainty about the role that Artificial Intelligence (AI) might play in Environmental Impact Assessment (EIA), including into research. AI large language model (LLM) chatbots have the potential to increase the efficiency of EIA research, but their outputs can create concerns. This paper investigates the potential time savings achievable using LLM chatbots to undertake a critical review of literature focussing on the use of AI in EIA. Using a combination of ChatGPT and Elicit, literature was reviewed to identify twelve key issues associated with the use of AI in EIA and this paper was prepared in three and a half days from initial conception. A protocol is developed to assist researchers in fact checking evidence delivered through Elicit (or other machine learning tools) which serves as a novel outcome of this research. Using comments from three peer reviewers allowed some more objective reflection on the credibility of the LLM chatbot-derived output, on the appropriateness of the time savings, and on the future research needed on the application of LLM chatbots in this context.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence (ai),environmental impact assessment (eia),future challenges,ethics,issues,environmental science(all),2* ,/dk/atira/pure/subjectarea/asjc/2300 |
| Faculty \ School: | Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA |
| UEA Research Groups: | Faculty of Science > Research Groups > Environmental Social Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 04 Mar 2024 18:32 |
| Last Modified: | 18 Mar 2024 10:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/94496 |
| DOI: | 10.1080/14615517.2024.2320591 |
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