AI and the transformation of social science research
By Igor Grossmann, M. Feinberg, D. C. Parker, Nicholas A. Christakis, Philip Tetlock, William A. Cunningham. Science, 2023
326 citations recorded by the source corpus.
Careful bias management and data fidelity are key Advances in artificial intelligence (AI), particularly large language models (LLMs), are substantially affecting social science research. These transformer-based machine-learning models pretrained on vast amounts of text data are increasingly capable of simulating human-like responses and behaviors (1, 2), offering opportunities to test theories and hypotheses about human behavior at great scale and speed. This presents urgent challenges: How can social science research practices be adapted, even reinvented, to harness the power of foundational AI? And how can this be done while ensuring transparent and replicable research?
Subfields: General Social Sciences, Safety Research, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/2addfdfbbc9db9cc23017ec2967b26484de6604c
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In General Social Sciences
Citations in the corpus
- The rise and potential of large language model based agents: a survey (2025, unknown): “Recent research on simulated societies has followed two primary lines, namely, exploring the boundaries of the collective intelligence capabilities of LLM-based agents [90, 115, 327, 330, 332] and using them to accelerate discoveries in the social sciences [16,438,439].”
- A survey on large language model based autonomous agents (2023, background): “ChemCrow [75], Boiko et al. [115], Grossmann et al. [122] Natural Science Education ChemCrow [75], CodeHelp [120], Boiko et al. [115], MathA-gent [117], Drori et al. [118] Engineering”
- Durably reducing conspiracy beliefs through dialogues with AI (2024, unknown): “Moreover, ouruseofGPT-4Turbo,afrontier,closed-source,pretrained,andfine-tunedlanguage model, presents challenges related to inter-pretability and replicability ( 69 – 71 ).”
- Artificial intelligence and illusions of understanding in scientific research (2024, unknown): “…including (but not limited to) race, ethnicity, gender, class, disability and/or relevant experiences such as background, training and career stage. experiences and perspectives ... offering a more accurate portrayal of human behaviour and social dynamics than those from conventional methods” 4 .”