by Will Kwong, University of Southern California
edited by Sadie Harley, reviewed by Andrew Zinin
Editors' notes
Credit: Pixabay/CC0 Public Domain
In a world increasingly shaped by artificial intelligence, the question of how machines make decisions under uncertain conditions grows more urgent every day.
How do we weigh competing values when outcomes are uncertain? What constitutes reasonable choice when perfect information is unavailable? These questions, once confined to academic philosophy, are now front and center as we delegate increasingly complex decisions to AI.
A new large language model (LLM) framework developed by Willie Neiswanger, assistant professor of computer science at the USC Viterbi School of Engineering and the USC School of Advanced Computing, along with students in the computer science department, combines classical decision theory and utility theory principles to significantly enhance AI's ability to face uncertainty and tackle those complex decisions.
Neiswanger's research was spotlighted at 2025's International Conference on Learning Representations and published on the arXiv preprint server. He recently discussed how AI handles uncertainty with USC News.
What are your thoughts on the difference between artificial and human intelligence?
Neiswanger: At present, human intelligence has various strengths relative to machine intelligence. However, machine intelligence also has certain strengths relative to humans, which make it valuable.
Large language models (LLMs)—AI systems trained on vast amounts of text that can understand and generate humanlike responses—for instance, can rapidly ingest and synthesize large amounts of information from reports or other data sources, and can generate at scale by simulating many possible futures or proposing a wide range of forecasted outcomes. In our work, we aim to take advantage of the strengths of LLMs while balancing them against the strengths and judgment of humans.
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