  This paper introduces A2C, a multi-stage collaborative decision framework
designed to enable robust decision-making within human-AI teams. Drawing
inspiration from concepts such as rejection learning and learning to defer, A2C
incorporates AI systems trained to recognise uncertainty in their decisions and
defer to human experts when needed. Moreover, A2C caters to scenarios where
even human experts encounter limitations, such as in incident detection and
response in cyber Security Operations Centres (SOC). In such scenarios, A2C
facilitates collaborative explorations, enabling collective resolution of
complex challenges. With support for three distinct decision-making modes in
human-AI teams: Automated, Augmented, and Collaborative, A2C offers a flexible
platform for developing effective strategies for human-AI collaboration. By
harnessing the strengths of both humans and AI, it significantly improves the
efficiency and effectiveness of complex decision-making in dynamic and evolving
environments. To validate A2C's capabilities, we conducted extensive simulative
experiments using benchmark datasets. The results clearly demonstrate that all
three modes of decision-making can be effectively supported by A2C. Most
notably, collaborative exploration by (simulated) human experts and AI achieves
superior performance compared to AI in isolation, underscoring the framework's
potential to enhance decision-making within human-AI teams.
