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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“By analyzing large-scale, unstructured data such as social media posts, news articles, or policy briefs, AI tools — particularly large language models (LLMs) — can be used to uncover the psychological barriers that exist in contexts beyond controlled laboratory studies [15,25,41].”
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“To delve deeper into this priority, we started by reflecting on how AI is already reshaping the knowledge infrastructure of academia itself, from how we collect data to how we teach, publish, and disseminate research (Grossmann et al., 2023).”
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“They are no longer merely experimental technological products but have become a key resource deeply integrated into scientific (Callaway, 2024; Grossmann et al., 2023), artistic (Our T2 Remake Team, 2024), and commercial processes (Maerten & Soydaner, 2024; Poireault, 2024).”
Chandra, Y., & Tan, J. (2026) · Public Administration Review · doi:10.1111/puar.70098 · Cited by 1
“…of artificial intelligence (AI) (Chandra and Feng 2025), have opened a new scientific infrastructure (Bail 2024) and methodological possibilities (Grossmann et al. 2023) to collect data, impersonate humans, automate data analyses and—as we argue later—to predict and manipulate competitive texts…”
AI-Generated Human Stimuli for Experimental Social Science
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Enhancing LLM-Based Data Annotation with Error Decomposition
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SocLitGen: An LLM-assisted framework for automated literature review in social sciences
Hu, S., Wei, Y., & Sun, S. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104885 · Cited by 1
Artificial intelligence in police research: a preliminary examination of feasibility and replication
McLean, K. D., Rojek, J. J., & Nix, J. (2026) · Journal of Experimental Criminology · doi:10.1007/s11292-026-09754-4 · Cited by 1
Decision science in the agentic era: Research frontiers on agentic AI and human–agent collaboration
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Large language models instantiate evolutionarily robust strategies of cooperation
Pal, S., Mallela, A., Pracher, L., Wei, C., Fu, F., Schnell, S. D., & Nowak, M. A. (2026) · PNAS Nexus · doi:10.1093/pnasnexus/pgag210 · Cited by 10
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“LLMs may contribute to social science research through synthetic data generation (81, 82) and qualitative theme identification (83).”
For Factors of AI Integration in Research: Case Study of Russian Laboratories
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Modern approaches to studying human interaction with generative artificial intelligence
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Large language models as human proxies
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Davinci the Dualist: The Mind–Body Divide in Large Language Models and in Human Learners
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“…Binz & Schulz, 2023; Mitchell, 2023; Sap et al., 2022), it demonstrably approximates human learning in several domains (e.g., Dillion et al., 2023; Grossmann et al., 2023; but cf. Park et al., 2024); some have even credited it with general intelligence (Bubeck et al., 2023; but cf. Marcus, 2022a).”
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“Language/text analyses allow for more objective observation of people’s natural expressions of their thoughts and feelings, thereby measuring psychology with less response bias and greater efficiency (Berger & Packard, 2022; Grossmann et al., 2023).”
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“(Grossmann et al. 2023) However, promises are usually accompanied by concerns.”
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“Thus, they are susceptible to socio-cultural biases and stereotypes that the majority of people possess ( Grossmann et al. 2023).”
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“These factors impose higher demands on the knowledge representation capabilities of such models (Cuskley et al., 2024; Grossmann et al., 2023).”
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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