Review
Open access
Oct 2026
From Results to Practice: A Human-LLM Collaboration Framework for Spanning Causal Machine Learning Knowledge Boundaries
A framework is proposed that conceptualizes large language models (LLMs) as dynamic boundary spanning objects and leverages different forms of human-LLM collaborations throughout the causal ML boundary spanning process, toward the goal of successful practice transformation.
Aaron Baird, Yu-Sen Xia, Gaurav Jetley et al.
· ACM Transactions on Manageme... · 0 citations