AI, Decision Science, and Psychological Theory in Decisions about People: A Case Study in Jury Selection

Abstract

AI theory and its technology is rarely consulted in attempted resolutions of social problems. Solutions often require that decision-analytic techniques be combined with expert systems. The emerging literature on combined systems is directed at domains where prediction of human behavior is not required. A foundational shift in AI presuppositions to intelligent agents working in collaboration provides an opportunity to explore efforts to improve performance of social institutions that depend on accurate prediction of human behavior. Professionals concerned with human outcomes make decisions that are intuitive or analytic or some combination of both. The relative efficacy of each decision type is described. Justifications and methodology are presented for combining analytic and intuitive agents in an expert system that supports professional decision making. Psychological grounds for allocation of functions to agents are reviewed. Jury selection, prototype domain, is described as a process typical of others that, at their core, require prediction of human behavior. The domain is used to demonstrate formal components, steps in construction, and challenges of developing and testing a hybrid system based on allocation of function. The principle that research taught us about allocation of function is the rational and predictive primacy of a statistical agent to an intuitive agent in construction of a production system. We learned that reverse of this principle is appropriate for identifying and classifying human responses to questions and generally dealing with unexpected events in a courtroom and elsewhere. This principle and approach should be paradigmatic of class of collaborative models that capitalizes on unique strengths of AI knowledge-based systems. The methodology used in courtroom is described along with history of project and implications for development of related AI systems. Empirical data are reported that portend possibility of impressive predictive ability in combined approach relative to other current approaches. Problems encountered and those remaining are discussed, including limits of empirical research and standards of validation. The system presented demonstrates challenges and opportunities inherent in developing and using AI-collaborative technology to solve social problems.