Applying HCAI in developing effective human-AI teaming: A perspective from human-AI joint cognitive systems

TL;DR

This paper discusses how human-AI teaming (HAT) can be effectively developed within a human-centered AI framework by proposing a conceptual model of human-AI joint cognitive systems (HAIJCS) to enhance collaboration.

Contribution

It introduces the HAIJCS framework to represent and implement HAT, bridging the gap between human-centered AI and collaborative human-AI systems.

Findings

HAT represents a paradigm shift in human-AI relationships.

HCAI principles are essential for designing effective HAT systems.

The HAIJCS framework aids in integrating AI as a collaborative teammate.

Abstract

Research and application have used human-AI teaming (HAT) as a new paradigm to develop AI systems. HAT recognizes that AI will function as a teammate instead of simply a tool in collaboration with humans. Effective human-AI teams need to be capable of taking advantage of the unique abilities of both humans and AI while overcoming the known challenges and limitations of each member, augmenting human capabilities, and raising joint performance beyond that of either entity. The National AI Research and Strategic Plan 2023 update has recognized that research programs focusing primarily on the independent performance of AI systems generally fail to consider the functionality that AI must provide within the context of dynamic, adaptive, and collaborative teams and calls for further research on human-AI teaming and collaboration. However, there has been debate about whether AI can work as a teammate with humans. The primary concern is that adopting the "teaming" paradigm contradicts the human-centered AI (HCAI) approach, resulting in humans losing control of AI systems. This article further analyzes the HAT paradigm and the debates. Specifically, we elaborate on our proposed conceptual framework of human-AI joint cognitive systems (HAIJCS) and apply it to represent HAT under the HCAI umbrella. We believe that HAIJCS may help adopt HAI while enabling HCAI. The implications and future work for HAIJCS are also discussed. Insights: AI has led to the emergence of a new form of human-machine relationship: human-AI teaming (HAT), a paradigmatic shift in human-AI systems; We must follow a human-centered AI (HCAI) approach when applying HAT as a new design paradigm; We propose a conceptual framework of human-AI joint cognitive systems (HAIJCS) to represent and implement HAT for developing effective human-AI teaming