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Why open-source generative AI models are an ethical way forward for science
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“Open-washing is also bad for research, because it means that researchers can no longer count on being able to tinker with models and architectures even if they are advertised as open source [54].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [54]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Though Generative AI may help researchers increase the external validity of their research designs, this may come at the cost of internal validity—or the capacity of different groups of researchers to reproduce or replicate each other’s results (2, 12).”
From Can Generative AI improve social science? (2024) · cites this paper as [2]
Experimental — accuracy not yet measuredIn the citing paper
“If social scientists become reliant upon proprietary models, we also risk tying our endeavors to the vicissitudes of corporate interests (2).”
From Can Generative AI improve social science? (2024) · cites this paper as [2]
In the citing paper
“The less black-box nature of these open-source models can help researchers make the exact version used in their works available to others (100), thus facilitating transparency and reproducibility.”
From Perils and opportunities in using large language models in psychological research (2024) · cites this paper as [100]
In the citing paper
“This means that even when updates address and mitigate detected biases, they also introduce the potential for “process reproducibility failure” (98) in the generated data and impede reproducibility, critical for scientific rigor (15, 100, 101).”
From Perils and opportunities in using large language models in psychological research (2024) · cites this paper as [100]
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Can Generative AI improve social science?
Bail, C. A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314021121 · Cited by 369
Durably reducing conspiracy beliefs through dialogues with AI
Costello, T. H., Pennycook, G., & Rand, D. G. (2024) · Science · doi:10.1126/science.adq1814 · Cited by 354
Synthetic Replacements for Human Survey Data? The Perils of Large Language Models
Bisbee, J., Clinton, J. D., Dorff, C., Kenkel, B., & Larson, J. M. (2024) · Political Analysis · doi:10.1017/pan.2024.5 · Cited by 209
ChatGPT: A Case Study on Copyright Challenges for Generative Artificial Intelligence Systems
Lucchi, N. (2024) · European Journal of Risk Regulation · doi:10.1017/err.2023.59 · Cited by 173
Velasquez, J. D., Franco, C. J., & Cadavid, L. (2023) · DYNA · doi:10.15446/dyna.v90n230.111700 · Cited by 171
Metadata
- Authors
- Spirling, A.
- Affiliations
- New York University (US)
- Corresponding author
- Spirling, Arthur
- ORCID
- Spirling, Arthur
- Venue
- Nature
- Published
- 2023-04-18
- Volume
- 616
- Issue
- 7957
- Pages
- 413-413
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4366277658
- Cited by
- 104
- References
- 0
- Citations by year
- 2026: 23 · 2025: 32 · 2024: 28 · 2023: 20
- FWCI
- 26.08
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- open-source models, generative AI, large language models, research ethics, reproducibility, transparency, scientific research
- MeSH terms
- Artificial Intelligence, Natural Language Processing, Research, Reproducibility of Results, Access to Information
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