Study categorizes human-AI teams into five distinct types

A new study published on arXiv analyzes 53 papers on human-AI teams to categorize them into distinct types. The research identifies five main clusters: AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity. These categories highlight unique combinations of team characteristics, suggesting that insights from different studies may not be directly transferable due to these disparate team types. The paper offers guidance for identifying human-AI team types and reporting them in research. AI

IMPACT This research provides a framework for understanding and categorizing human-AI teams, which could help researchers better synthesize findings and design future human-AI collaborations.

RANK_REASON The item is a research paper published on arXiv detailing a study and its findings. [lever_c_demoted from research: ic=1 ai=1.0]

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• Ad-hoc Dependency
• Ad-hoc Forced Dependency
• AI Assistant
• alphaXiv
• arXiv
• CatalyzeX Code Finder for Papers
• Connected Papers
• CORE Recommender
• DagsHub
• Gotit.pub
• Group Equanimity
• Hugging Face
• Human-AI teaming
• Influence Flower
• Litmaps
• Paired Equanimity
• ScienceCast
• scite Smart Citations

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