Toward a Quality Model for Hybrid Intelligence Teams

Abstract

Hybrid Intelligence (HI) is an emerging paradigm in which artificial intelligence (AI) augments human intelligence. The current literature lacks systematic models that guide the design and evaluation of HI systems. Further, discussions around HI primarily focus on technology, neglecting the holistic human-AI ensemble. In this paper, we take the initial steps toward the development of a quality model for characterizing and evaluating HI systems from a human-AI teams perspective. We conducted a study investigating the adequacy of properties commonly associated with effective human teams to describe HI. Our study, featuring the insights of 50 HI researchers, shows that various human team properties, including boundedness, interdependence, competency, purposefulness, initiative, normativity, and effectiveness, are important for HI systems. Our study also reveals limitations in applying certain human team properties, such as coaching, rewards, and recognition, to HI systems due to the inherent human-AI asymmetry.