Collaborative Causal Sensemaking: Closing the Complementarity Gap in Human-AI Decision Support

📰 ArXiv cs.AI

Collaborative causal sensemaking can close the complementarity gap in human-AI decision support by training LLM-based agents as partners in decision-making

advanced Published 26 Mar 2026

Action Steps

Identify the limitations of current LLM-based agents in human-AI decision support
Develop training methods that focus on collaborative sensemaking and causal explanation
Implement LLM-based agents that can co-construct causal explanations with human experts
Evaluate the performance of human-AI teams in high-stakes decision-making environments

Who Needs to Know This

Data scientists, AI engineers, and product managers can benefit from this approach as it enhances human-AI collaboration in high-stakes decision-making environments

Key Insight

💡 Training LLM-based agents as partners in collaborative sensemaking can close the complementarity gap in human-AI decision support