Nisbett, N and Spaiser, V orcid.org/0000-0002-5892-245X (2023) How convincing are AI-generated moral arguments for climate action? Frontiers in Climate, 5 (1193350). ISSN 2624-9553
Abstract
Mobilizing broad support for climate action is paramount for solving the climate crisis. Research suggests that people can be persuaded to support climate action when presented with certain moral arguments, but which moral arguments are most convincing across the population? With this pilot study, we aim to understand which types of moral arguments based on an extended Moral Foundation Theory are most effective at convincing people to support climate action. Additionally, we explore to what extent Generative Pre-trained Transformer 3 (GPT-3) models can be employed to generate bespoke moral statements. We find statements appealing to compassion, fairness and good ancestors are the most convincing to participants across the population, including to participants, who identify as politically right-leaning and who otherwise respond least to moral arguments. Negative statements appear to be more convincing than positive ones. Statements appealing to other moral foundations can be convincing, but only to specific social groups. GPT-3-generated statements are generally more convincing than human-generated statements, but the large language model struggles with creating novel arguments.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Nisbett and Spaiser. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | Moral Foundations Theory, climate change, climate action, climate communication, AI, GPT-3 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) > School of Politics & International Studies (POLIS) (Leeds) |
Funding Information: | Funder Grant number UKRI (UK Research and Innovation) MR/V021141/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Jul 2023 11:32 |
Last Modified: | 10 Jul 2023 11:32 |
Status: | Published |
Publisher: | Frontiers Media |
Identification Number: | 10.3389/fclim.2023.1193350 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:201347 |