An Agile New Research Framework for Hybrid Human-AI Teaming: Trust, Transparency, and Transferability

Abstract

We propose a new research framework by which the nascent discipline of human-AI teaming can be explored within experimental environments in preparation for transferal to real-world contexts. We examine the existing literature and unanswered research questions through the lens of an Agile approach to construct our proposed framework. Our framework aims to provide a structure for understanding the macro features of this research landscape, supporting holistic research into the acceptability of human-AI teaming to human team members and the affordances of AI team members. The framework has the potential to enhance decision-making and performance of hybrid human-AI teams. Further, our framework proposes the application of Agile methodology for research management and knowledge discovery. We propose a transferability pathway for hybrid teaming to be initially tested in a safe environment, such as a real-time strategy video game, with elements of lessons learned that can be transferred to real-world situations.

Human-AI collaboration by design

“…HAIC emerges as a promising paradigm to harness the complementary strengths of humans and AI for problem-solving, insight generation, and value creation. It has been referred to in various contexts as hybrid intelligence (Dellermann et al, 2019), hybrid human-AI teaming (Caldwell et al, 2022), and superteams (integration of AI into teams) (Deloitte, 2020). Despite its potential, the interdisciplinary socio-technological field of HAIC is rife with unanswered questions, presenting high risks when misapplied.…”

Exploring the landscape of trustworthy artificial intelligence: Status and challenges

“…The path towards establishing trustworthy human-AI teaming, spanning from initial conception to the formation of high-performing teams, encompasses a wide array of areas including bi-directional situational awareness, human-AI interaction, intelligent decision-making, and human-AI operations [20]. Furthermore, it is noted that this research landscape is defined more by open research questions than by existing knowledge.…”

“…Furthermore, it is noted that this research landscape is defined more by open research questions than by existing knowledge. Many of the questions that researchers will confront along this path are likely yet to be posed, let alone answered [20].…”