Alignment--Process--Outcome: Rethinking How AIs and Humans Collaborate
This work provides a theoretical framework for understanding the complex dynamics of collaboration, which could benefit researchers and practitioners designing and analyzing collaborative systems involving humans and AI.
This paper re-examines collaboration by proposing a unified dynamic view of the relationships among alignment, process, and outcome, rather than reducing collaboration to outcome quality or treating alignment as the sole objective. It introduces two complementary lenses—task and intent—to clarify the structural relationships among alignment, decision-making, and trajectory structure across Human-Human, AI-AI, and Human-AI settings.
In real-world collaboration, alignment, process structure, and outcome quality do not exhibit a simple linear or one-to-one correspondence: similar alignment may accompany either rapid convergence or extensive multi-branch exploration, and lead to different results. Existing accounts often isolate these dimensions or focus on specific participant types, limiting structural accounts of collaboration. We reconceptualize collaboration through two complementary lenses. The task lens models collaboration as trajectory evolution in a structured task space, revealing patterns such as advancement, branching, and backtracking. The intent lens examines how individual intents are expressed within shared contexts and enter situated decisions. Together, these lenses clarify the structural relationships among alignment, decision-making, and trajectory structure. Rather than reducing collaboration to outcome quality or treating alignment as the sole objective, we propose a unified dynamic view of the relationships among alignment, process, and outcome, and use it to re-examine collaboration structure across Human-Human, AI-AI, and Human-AI settings.