arXiv:2606.04779v1 Announce Type: new Abstract: Complementarity is the case in which a human--AI interaction (HAI) outperforms the best prediction benchmark available among its members. Although this idea is central in HAI research, formal work on complementarity remains limited. Existing frameworks do not model how agents' predictions compose into workflow-sensitive multi-agent protocols. We close this gap by introducing a tree-based formalization of complementarity in multi-agent HAI. An HAI protocol is represented by an ordered agent-role configuration together with a rooted planar binary t
The proliferation of AI systems necessitates a deeper understanding of human-AI collaboration beyond simple benchmarks, leading researchers to formalize complementarity.
Formalizing complementarity in human-AI interactions is crucial for designing AI systems that genuinely augment human capabilities, leading to more effective and productive collaboration.
The introduction of a tree-based formalization provides a structured method to analyze and optimize multi-agent human-AI protocols, enabling more nuanced system design.
- · AI ethicists
- · Human-AI interface designers
- · Software developers
- · Consulting firms
- · Companies relying on simplistic AI integration
- · Inefficient multi-agent systems
Improved design and performance of human-AI collaborative systems.
Increased adoption of AI in complex decision-making processes where human oversight is critical.
New regulatory frameworks emerging to ensure ethical and effective human-AI teamwork across critical sectors.
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Read at arXiv cs.AI