Can Generative AI Improve Social Science?

Artificial intelligence 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 transform social science research. In the first section of this article, I assess the potential of Generative AI to improve online experiments, agent-based models, and automated content analyses. I also discuss whether these tools may help social scientists perform literature reviews, identify novel research questions, and develop hypotheses to explain them. Next, I evaluate whether Generative AI can help social scientists with more mundane tasks such as acquiring advanced programming skills or writing more effective prose. In the second section of this article I discuss the limitations of Generative AI as well as how these tools might be employed by researchers in an ethical manner. I discuss how bias in the processes and data used to train these tools can negatively impact social science research as well as a range of other challenges related to accuracy, reproducibility, interpretability, and efficiency. I conclude by highlighting the need for increased collaboration between social scientists and artificial intelligence researchers— not only to ensure that such tools are used in a safe and ethical manner, but also because the progress of artificial intelligence may require deeper understanding of theories of human behavior.,,

Ryan Watkins

Professor at George Washington University

I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.