arXiv:2607.02198v1 Announce Type: cross Abstract: Human-AI teaming has received increasing attention in the literature. However, the range of studies conducted in multiple domains make it difficult to understand what types of teams are being studied, and in what ways are they similar/different from one another. In this study, we analyse 53 papers on human-AI teams and categorise them into five main clusters based on psychological taxonomies of teaming; AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity. Each cluster represents a unique combinatio
The proliferation of AI systems across various applications necessitates a clearer understanding of human-AI interaction models to optimize performance and integration.
A robust taxonomy of human-AI teaming allows for more effective design, deployment, and regulation of AI systems, impacting productivity and safety across numerous sectors.
This research provides a structured framework for categorizing human-AI teams, moving the field beyond anecdotal observations to a more systematic approach for development and research.
- · AI developers
- · Human-AI collaboration platforms
- · Workforce training providers
- · Automation industries
- · Inefficient AI integration strategies
- · Laggard industries in AI adoption
Improved human-AI workflows and increased efficiency in tasks involving AI systems.
Development of specialized AI training programs tailored to different teaming archetypes, enhancing human proficiency with AI.
Potential for new ethical and regulatory frameworks specifically designed for various human-AI team configurations, addressing accountability and decision-making.
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Read at arXiv cs.AI