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How should one design collective decision-making processes capable of handling enormous sets of alternatives like all possible policies, bills, or statements? I argue that this challenge can be addressed through a framework called generative social choice, which fuses the rigor of social choice theory with the flexibility and power of large language models. I then explore an application of generative social choice to the problem of identifying a proportionally representative slate of opinion statements. This includes a discussion of desired properties, an algorithm that provably achieves them, an implementation using GPT, and insights from an end-to-end pilot. By providing guarantees, generative social choice could alleviate concerns about AI-mediated governance and help unlock its potential.

Ariel Procaccia is the Alfred and Rebecca Lin Professor of Computer Science at Harvard University. He works on a broad and dynamic set of problems related to AI, algorithms, economics, and society. He has helped create systems and platforms that are widely used to solve everyday fair division problems, resettle refugees, distribute food, and select citizens’ assemblies. To make his research accessible to the public, he has written numerous opinion and exposition pieces for publications such as The New York Times, The Washington Post, Bloomberg, Wired, and Scientific American. He is an ACM Fellow (2025), a AAAI Fellow (2024), and a recipient of the ACM SIGecom Mid-Career Award (2024), Social Choice and Welfare Prize (2020), Guggenheim Fellowship (2018), IJCAI Computers and Thought Award (2015), and Sloan Research Fellowship (2015).