The Institute for Analytical Sociology Seminar

Venue: KO301 & Online on Zoom (see Zoom link in the end of the email)

Monday, October 2 @ 14:30CET

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Accelerating (social) science with human-aware artificial intelligence

James Evans

University of Chicago

Abstract:
Artificial intelligence (AI) models trained on published scientific findings have been used to invent valuable materials and targeted therapies, but they typically ignore the human scientists who continually alter the landscape of discovery. Here we show that incorporating the distribution of human expertise by training unsupervised models on simulated inferences cognitively available to experts dramatically improves (up to 400%) AI prediction of future discoveries beyond those focused on research content alone, especially when relevant literature is sparse. These models succeed by predicting human predictions and the scientists who will make them. By tuning human-aware AI to avoid the crowd, we can generate scientifically promising “alien” hypotheses unlikely to be imagined or pursued without intervention until the distant future, which hold promise to punctuate scientific advance beyond questions currently pursued. I also explore the creation of other kinds of data-driven, machine learned "digital doubles" (e.g., LLM-based social agents) that facilitate cycles of semi-automated virtual and staged experiments tuned to reveal social and scientific insights and generate social and material technologies. Accelerating human discovery or probing its blind spots, I show how human-aware and complementary AI enables us to move toward and beyond the contemporary (social) scientific frontier.

Join Zoom Meeting

https://liu-se.zoom.us/j/65535789369

Meeting ID: 655 3578 9369

James Evans is the Max Palevsky Professor of History and Civilization, Sociology, Director of Knowledge Lab, and Founding Faculty Director of Computational Social Science at the University of Chicago and the Santa Fe Institute. Evans' research uses large-scale data, machine learning and generative models to understand how collectives think and what they know. This involves inquiry into the emergence of ideas, shared patterns of reasoning, and processes of attention, communication, agreement, and certainty. Thinking and knowing collectives like science, Wikipedia or the Web involve complex networks of diverse human and machine intelligences, collaborating and competing to achieve overlapping aims. Evans' work connects the interaction of these agents with the knowledge they produce and its value for themselves and the system. 

Evans designs observatories for understanding that fuse data from text, images and other sensors with results from interactive crowd sourcing and online experiments. Much of Evans' work has investigated modern science and technology to identify collective biases, generate new leads taking these into account, and imagine alternative discovery regimes. He has identified R&D institutions that generate more and less novelty, precision, density and robustness. Evans also explores thinking and knowing in other domains ranging from political ideology to popular culture. His work has been published in Nature, Science, PNAS, American Sociological Review, American Journal of Sociology and many other outlets.