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

Key Takeaways

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

Full Article

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

Abstract:
arXiv:2512.07801v5 Announce Type: replace-cross Abstract: LLM-based agents are increasingly deployed for expert decision support, yet human-AI teams in high-stakes settings do not yet reliably outperform the best individual. We argue this complementarity gap reflects a fundamental mismatch: current agents are trained as answer engines, not as partners in the collaborative sensemaking through which experts actually make decisions. Sensemaking (the ability to co-construct causal explanations, surf

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