Predictive models for human–AI nexus in group decision making. Issue 1 (17th May 2022)

• Record Type: Journal Article
• Authors: Askarisichani, Omid; Bullo, Francesco; Friedkin, Noah E.; Singh, Ambuj K.
• Is Part Of: Annals of the New York Academy of Sciences. Volume 1514:Issue 1(2022)
• Page Start: 70
• Page End: 81
• Publication Date: 2022-05-17
• Subjects: decision making -- human—AI teams -- machine learning; Medical sciences -- Periodicals; Medicine -- Periodicals; Science -- Periodicals; 610
• DOI: 10.1111/nyas.14783
• Languages: English
• ISSNs: 0077-8923

Abstract

Abstract: Machine learning (ML) and artificial intelligence (AI) have had a profound impact on our lives. Domains like health and learning are naturally helped by human–AI interactions and decision making. In these areas, as ML algorithms prove their value in making important decisions, humans add their distinctive expertise and judgment on social and interpersonal issues that need to be considered in tandem with algorithmic inputs of information. Some questions naturally arise. What rules and regulations should be invoked on the employment of AI, and what protocols should be in place to evaluate available AI resources? What are the forms of effective communication and coordination with AI that best promote effective human–AI teamwork? In this review, we highlight factors that we believe are especially important in assembling and managing human–AI decision making in a group setting. Abstract : Our simplified representation in Figure 1 is now augmented to include (1) the presence of AI agents in the group, and (2) the adoption of ML models to predict the behavior of a human+AI group. In this review, we confine our ML modeling efforts to the specific tasks of predicting the emergence of influence systems (the left big arrow) and the prediction of decisions under multiple risk/reward scenarios (the right big arrow). Note that ML techniques can be utilized broadly in other tasks in the group environment, such as deciding optimal group composition and the design of interventions.