Artificial Social Intelligence

A conference on the potential connections between sociology and work in artificial intelligence was sponsored by the National Science Foundation and held at the National Center for Supercomputing Applications at the University of Illinois in May 1993. The Participants were:

• William Sims Bainbridge, NSF Sociology Program
• Edward E. Brent, University of Missouri, Columbia
• Kathleen M. Carley, Carnegie-Mellon University
• David R. Heise, Indiana University
• Michael W. Macy, Brandeis University
• Barry Markovsky, University of Iowa
• John Skvoretz, University of South Carolina

Abstract

Sociologists have begun to explore the gains for theory and research that might be achieved by artificial intelligence technology: symbolic processors, expert systems, neural networks, genetic algorithms, and classifier systems. The first major accomplishments of artificial social intelligence (ASI) have been in the realm of theory, where these techniques have inspired new theories as well as helping to render existing theories more rigorous. Two application areas for which ASI holds great promise are the sociological analysis of written texts and data retrieval from the forthcoming Global Information Infrastructure. ASI has already been applied to some kinds of statistical analysis, but how competitive it will be with more conventional techniques remains unclear. To take advantage of the opportunities offered by ASI, sociologists will have to become more computer literate and will have to reconsider the place of programming and computer science in the sociological curriculum. ASI may be a revolutionary approach with the potential to rescue sociology from the doldrums into which some observers believe it has fallen.

The full text of this report was published in Annual Review of Sociology in 1994 (vol. 20, pp. 407-436).