Value attributed to text-based archives generated by artificial intelligence

Darda, K., Carre, M. and Cross, E. (2023) Value attributed to text-based archives generated by artificial intelligence. Royal Society Open Science, 10(2), 220915. (doi: 10.1098/rsos.220915) (PMID:36778947) (PMCID:PMC9905996)

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Abstract

Openly available natural language generation (NLG) algorithms can generate human-like texts across domains. Given their potential, ethical challenges arise such as being used as a tool for misinformation. It is necessary to understand both how these texts are generated from an algorithmic point of view, and how they are evaluated by a general audience. In this study, our aim was to investigate how people react to texts generated algorithmically, whether they are indistinguishable from original/human-generated texts, and the value people assign these texts. Using original text-based archives, and text-based archives generated by artificial intelligence (AI), findings from our preregistered study (N = 228) revealed that people were more likely to preserve original archives compared with AI-generated archives. Although participants were unable to accurately distinguish between AI-generated and original archives, participants assigned lower value to archives they categorized as AI-generated compared with those they categorized as original. People's judgements of value were also influenced by their attitudes toward AI. These findings provide a richer understanding of how the emergent practice of automated text creation alters the practices of readers and writers, and have implications for how readers' attitudes toward AI affect the use and value of AI-based applications and creations.

Item Type:Articles
Additional Information:The authors gratefully acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement number 677270 to E.S.C.), and the Leverhulme Trust (PLP-2018-152 to E.S.C).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Darda, Miss Kohinoor and Cross, Professor Emily
Authors: Darda, K., Carre, M., and Cross, E.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Royal Society Open Science
Publisher:The Royal Society
ISSN:2054-5703
ISSN (Online):2054-5703
Published Online:08 February 2023
Copyright Holders:Copyright © 2023 The Authors
First Published:First published in Royal Society Open Science 10(2): 220915
Publisher Policy:Reproduced under a Creative Commons License
Data DOI:10.6084/m9.figshare.c.6405757

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
303930SOCIAL ROBOTSEmily CrossEuropean Research Council (ERC)677270Centre for Neuroscience
304215Philip Leverhulme Prize - ECEmily CrossLeverhulme Trust (LEVERHUL)PLP-2018-152Centre for Neuroscience