Mills, S. orcid.org/0000-0002-6698-0983 and Sætra, H.S. (2025) Algorithms in the room: AI, representation, and decisions about sustainable futures. Technovation, 147. 103304. ISSN: 0166-4972
Abstract
This article considers the role of generative AI technologies, such as large language models (LLMs), in promoting the views of underrepresented groups. We are specifically concerned with the role AI could play in encouraging powerful decision-makers—often leading politicians and businesspeople in Western nations—to consider the perspectives of underrepresented groups when making decisions about sustainable development. Some suggest generative AI could offer decision-makers perspectives they had previously not considered, leading to more equitable and innovative policy approaches, and supporting several of the United Nations' Sustainable Development Goals (SDGs). We critique this perspective. Groups may be underrepresented in sustainable development decision-making because of individual cognitive and organisational information-processing limitations (‘omitted, but not opposed’), and because of opposition which remains even if these limitations are overcome (‘opposed, whether omitted or not’). We outline how these ‘categories of omission’ shape the opportunities and risks created by generative AI in representative sustainability.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Generative AI; Sustainable development goals; Representation; Decision-making |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Economics Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Aug 2025 13:44 |
Last Modified: | 06 Aug 2025 13:44 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.technovation.2025.103304 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:230059 |