ArticleProceedings of the National Academy of Sciences of the United States of America2024
Can Generative AI improve social science?
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- The rise of large language models and the direction and impact of US federal research funding.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- AI agents can coordinate via majority-following beyond human scale.Science advances · 2026Article
- Article
- Can AI help reduce prejudice? Evaluating the effectiveness of AI-powered personalized persuasion on support for transgender rights.PNAS nexus · 2026Article
- Evaluating the statistical realism of LLM-generated social science data.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Moral stereotyping in large language models.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- SciSciGPT: advancing human-AI collaboration in the science of science.Nature computational science · 2026Article
- A large-scale evaluation of commonsense knowledge in humans and large language models.PNAS nexus · 2026Article
- The impact of generative AI on social media: an experimental study.Scientific reports · 2026Article
- Why can't epidemiology be automated (yet)?International journal of epidemiology · 2026Article
- Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies.EPJ data science · 2026Article
- From institutional trust to AI adoption: a trust transfer and risk perception model of AI-enabled public service acceptance in China's digital government context.Frontiers in psychology · 2026Article
- Unmasking conversational bias in AI multiagent systems.PloS one · 2026Article
- ChatGPT does not replicate human moral judgments: the importance of examining metrics beyond correlation to assess agreement.Scientific reports · 2025Article
- Artificial Intelligence Approximates Human Affect Ratings of Cannabis Images.medRxiv : the preprint server for health sciences · 2025Article
- The Impact of Blame Attribution on Moral Contagion in Controversial Events.Entropy (Basel, Switzerland) · 2025Article
- A Systematic Review of User Attitudes Toward GenAI: Influencing Factors and Industry Perspectives.Journal of Intelligence · 2025Review
- Emergent social conventions and collective bias in LLM populations.Science advances · 2025Article
- Intersectional analysis for science and technology.Nature · 2025Review
- Measuring gender and racial biases in large language models: Intersectional evidence from automated resume evaluation.PNAS nexus · 2025Article
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1 author.
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Abstract
Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative. I examine how bias in the data used to train these tools can negatively impact social science research-as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.