The advance of artificial intelligence (AI) has opened new dimensions in a variety of fields, and social science research is no exception. Recent developments in Large Scale Language Models (LLMs) and Structural Causal Models (SCMs) are redefining traditional social research methods, promising a revolution in the way we understand and analyze human behavior and social structures. This blog explores the significant contributions of three recent studies that illustrate the potential and challenges of applying AI in the social sciences.

Models as Scientists and Subjects

Manning and Horton (2024) present a bold approach where LLMs not only assist in hypothesis generation and testing but act as experimental subjects themselves. By integrating LLMs with SCMs, their study demonstrates how complex human interactions can be simulated in controlled environments to test theories and social models more efficiently and at lower cost. This methodology opens a field of social experimentation that was previously impractical due to ethical or logistical constraints.

Korinek (2023) and Ziems et al. (2023) offer complementary and sometimes contrasting perspectives on the use of LLMs in social research. Korinek focuses on how LLMs can improve efficiency in specific tasks such as writing, data analysis, and mathematical derivations, greatly facilitating the economic research process. However, he cautions against the need for human supervision to ensure the accuracy and relevance of AI-generated results.

On the other hand, Ziems et al. evaluate the performance of LLMs on classification and generation tasks, finding that, although useful, LLMs do not always outperform human or task-specifically tuned models. This finding is crucial because, while LLMs can speed up the research process, their ability to completely replace human analysis remains limited.

Integrating AI in Social Science: Challenges and Opportunities

The integration of AI into social science research presents both opportunities and challenges. LLMs, such as those used by Manning and Horton, enable a new form of social experimentation that could accelerate the discovery and validation of social theories. However, as Korinek and Ziems et al. point out, reliance on AI for social data interpretation and analysis should be handled with caution due to potential problems such as data bias and variability of results.

Ethical and Methodological Considerations

The use of AI in social science also raises significant ethical and methodological questions. The ability of LLMs to act as subjects and scientists in social studies suggests a future where AI could not only assist but also direct social research. This requires deep reflection on the role of human autonomy in science and the ethical limits of social simulation.


AI is set to transform social science research, offering powerful tools to simulate, analyze, and understand complex social dynamics. However, the success of this integration will depend on how researchers manage the challenges inherent in the technology, including accuracy, bias, and the ethics of automation in social research.


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