Predictive models for human–AI nexus in group decision making

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. K E Y W O R D Sdecision making, human-AI teams, machine learning Over the last decades, scientists have made meaningful headway in understanding collective group behavior of humans. Researchers have examined social processes of groups on single issues and sequences of issues, and have understood the implications of these social 70

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“…For example, fairness in hiring, lending, and imprisonment is related to AI decision-making applications [15]. Also, decision-making algorithms operate in complex socio-technical environments (for example, human-AI interactions make a significant part of risky decision-making in health and learning [20]). Human-AI interfaces will become increasingly widespread as ML algorithms are practical in real-world settings to help to improve human decision-making [21].…”