The Past, Present, and Future of Computational Social Science

SODAS Lecture with Christopher Bail, Duke University. 

Abstract

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet we are only beginning to understand how such tools might influence social science research. I will 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. Secondly, I will discuss the many limitations of Generative AI.

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 will 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.