The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
AI and the transformation of social science research
Abstract
Careful bias management and data fidelity are key
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In the citing paper
“Researchers have also recently noted GPT’s ability to help with computational social science tasks (49–54), detect misinformation (55), infer politicians’ ideologies (56), write persuasive political arguments (57), respond to patient questions (58), simulate human research participants (59–61), and model collective behavior (56).”
From GPT is an effective tool for multilingual psychological text analysis (2024) · cites this paper as [53]
In the citing paper
“Researchers who want to use LLMs to impersonate biased groups, for example, may discover these tools are unwilling to perform such roles because they have been fine-tuned according to the normative preferences of highly educated liberals who may have more concern about the protection of marginalized groups than others (12, 64).”
From Can Generative AI improve social science? (2024) · cites this paper as [12]
In the citing paper
“Though such guard-rails probably improve the safety of Generative AI tools for public use, they may impede the ability of social scientists to leverage bias for research purposes (12).”
From Can Generative AI improve social science? (2024) · cites this paper as [12]
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 [12]
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Self-citation
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“Language AI has become a new analytical tool across the humanities (Terras et al. 2016) and social sciences (Grossmann et al. 2023), but it has yet to gain traction in socio-cultural anthropology.”
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AI Socialization Project Management Framework
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Hou, D. X., Thapa, S., & Tay, L. (2026) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459251398713
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Syme, K., Motos, N., & Placek, C. D. (2026) · Royal Society Open Science · doi:10.1098/rsos.251766
Schück, F. (2026) · Frontiers in Water · doi:10.3389/frwa.2026.1749745
“Grossmann et al. (2023) argue that the LLMs’ ability to mimic human behavior could redefine social science, reducing the need for using other methods such as crowdsourcing.”
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Zhao, N., & Li, L. (2026) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.70068 · Cited by 1
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Varnum, M. E. W., & Grossmann, I. (2026) · Advances in experimental social psychology · doi:10.1016/bs.aesp.2026.03.004
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Hagemeier, D., & Letmathe, P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-17618-9_44
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Metadata
- Authors
- Grossmann, I., Feinberg, M., Parker, D. C., Christakis, N. A., Tetlock, P. E., & Cunningham, W. A.
- Affiliations
- University of Waterloo (CA)
- University of Toronto (CA)
- Yale University (US)
- University of Pennsylvania (US)
- ORCID
- Venue
- Science
- Published
- 2023-06-16
- Volume
- 380
- Issue
- 6650
- Pages
- 1108-1109
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4380763235
- Cited by
- 377
- References
- 10
- Citations by year
- 2026: 110 · 2025: 156 · 2024: 92 · 2023: 19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, social science research, research transformation, algorithmic bias, data fidelity, artificial intelligence in research
- MeSH terms
- Artificial Intelligence, Humans, Social Sciences, Bias
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