Questionable devices: Applying a large language model to deliberate carbon removal

This paper presents a device-centred approach to deliberation, developed in deliberative workshops appraising methods for removing carbon dioxide from the air. Our approach involved deploying the Large Language Model application ChatGPT (sometimes termed “generative AI”) to elicit questions and generate texts about carbon removal. We develop the notion of the “questionable” device to foreground the informational unruliness ChatGPT introduced into the deliberations. The analysis highlights occasions where the deliberative apparatus became a focus of collective critique, including over: issue definitions, expert-curated resources, lay identities and social classifications. However, in this set-up ChatGPT was all too often engaged unquestioningly as an instrument for informing discussion; its instrumental lure disguising the unruliness it introduced into the workshops. In concluding, we elaborate the notion of questionable devices and reflect on the way carbon removal has been “devised” as a field in want of informed deliberation. • Explores roles for large language model applications, like ChatGPT, in participatory research and policy deliberation. • Develops the concept of “questionable devices” to analyse how ChatGPT introduced unruliness into deliberative practice. • Argues that closer attention to devices may empower participants to engage politically with deliberative processes. • Reflects on the way that carbon removal has been “devised” as a field in want of informed deliberation.

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Publication Details

Journal
Environmental Science & Policy
Published
2024-11-06
DOI
https://doi.org/10.1016/j.envsci.2024.103940
Citations
2
Primary Topic
Sustainability and Climate Change Governance
Type
article
Field-Weighted Citation Impact
0.57
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article

Questionable devices: Applying a large language model to deliberate carbon removal

Laurie Waller, David Moats, Emily Cox, Rob Bellamy
2 citations
Environmental Science & Policy
Sustainability and Climate Change Governance
0.57
article

Questionable devices: Applying a large language model to deliberate carbon removal

Laurie Waller, David Moats, Emily Cox, Rob Bellamy
article en
2 citations

Abstract

This paper presents a device-centred approach to deliberation, developed in deliberative workshops appraising methods for removing carbon dioxide from the air. Our approach involved deploying the Large Language Model application ChatGPT (sometimes termed “generative AI”) to elicit questions and generate texts about carbon removal. We develop the notion of the “questionable” device to foreground the informational unruliness ChatGPT introduced into the deliberations. The analysis highlights occasions where the deliberative apparatus became a focus of collective critique, including over: issue definitions, expert-curated resources, lay identities and social classifications. However, in this set-up ChatGPT was all too often engaged unquestioningly as an instrument for informing discussion; its instrumental lure disguising the unruliness it introduced into the workshops. In concluding, we elaborate the notion of questionable devices and reflect on the way carbon removal has been “devised” as a field in want of informed deliberation. • Explores roles for large language model applications, like ChatGPT, in participatory research and policy deliberation. • Develops the concept of “questionable devices” to analyse how ChatGPT introduced unruliness into deliberative practice. • Argues that closer attention to devices may empower participants to engage politically with deliberative processes. • Reflects on the way that carbon removal has been “devised” as a field in want of informed deliberation.

Environmental Science & PolicyVol. 162
University of Manchester (GB), University of Oxford (GB), National Consumer Research Centre (FI)
Openalex Percentile: Top 33%
Sustainability and Climate Change Governance
0.57
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Questionable devices: Applying a large language model to deliberate carbon removal — Laurie Waller, David Moats, et al. · Environmental Science & Policy (2024) | TGRS Research Map | TGRS