Can Generative AI improve social science?

Может ли генеративный ИИ улучшить социальные науки?
Christopher A. Bail
2024-05-09

Generative AIagent-based modelsautomated content analysisonline experimentssurvey research
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.
1
Biases in training data for generative AI can negatively impact social science research results.
2
Creating open-source infrastructure for human behavior research can help address many limitations of generative AI and ensure broad access to high-quality research tools.
3
Generative AI can potentially improve multiple social science methods, including surveys, online experiments, automated content analysis, and agent-based models.
4
Generative AI poses additional challenges for social science such as ethical concerns, replication difficulties, environmental impact, and proliferation of low-quality research.
5
Understanding social forces guiding human behavior is necessary for progress in AI and motivates social scientists' involvement in AI development.

Generative AI tools for producing realistic text, images, and other human-like outputs

Their potential to improve social science research methods (survey research, online experiments, automated content analysis, agent-based models, etc.) and the limitations/impacts (bias, ethics, replication, environmental cost, proliferation of low-quality research) and how open-source research infrastructure can mitigate these issues

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2024-05-09
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Christopher A. Bail
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