Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note3 min read

What Types of Human-AI Teams Exist?

Source

arXiv

arxiv.org/abs/2607.02198v1

Read the source

Source

Plain-Language Brief

The authors analyze 53 papers about human-AI teams and categorize them into five main clusters based on psychological taxonomies of teaming.

What the Document Says

  • Human-AI teaming is a topic that has gained attention in research.
  • There are multiple domains where studies on human-AI teams have been conducted, making it challenging to understand the types of teams being studied and their similarities/differences.
  • The authors analyzed 53 papers on human-AI teams and grouped them into five main clusters based on psychological taxonomies.
  • The five clusters are AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity.
  • Each cluster represents a unique combination of holistic team-level characteristics.
  • The authors question if insights from different papers on human-AI teams are truly transferable due to the variety of team types studied under the same definition.
  • They provide guidance for identifying the types of human-AI teams in research work, a checklist for reporting human-AI teams, and suggestions for further synthesizing the field.

Key Concepts Mentioned

  • human-AI teaming
  • psychological taxonomies of teaming
  • AI Assistant
  • Ad-hoc Dependency
  • Ad-hoc Forced Dependency
  • Paired Equanimity
  • Group Equanimity

Claims Made by the Document

  • Human-AI teaming has received increasing attention in the literature.
  • Multiple domains make it challenging to understand what types of teams are being studied and their similarities/differences.
  • The authors categorize papers on human-AI teams into five main clusters based on psychological taxonomies.
  • Each cluster represents a unique combination of holistic team-level characteristics.
  • The authors question if insights from different papers on human-AI teams are truly transferable.

Evidence Used

  • Analysis of 53 papers on human-AI teams.
  • Grouping of papers into five main clusters based on psychological taxonomies.

Methods / Approach

  • Analysis and categorization of 53 papers on human-AI teams based on psychological taxonomies

Limitations Stated by the Authors

  • The authors question if insights from different papers on human-AI teams are truly transferable due to the variety of team types studied.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The analysis is based on only 53 papers, which might not represent the entire field of human-AI teaming.
  • The study does not provide a detailed explanation of how the psychological taxonomies were applied to categorize the papers.

Open Questions

  • Are there more than five main clusters of human-AI teams?
  • How do the insights from different papers on human-AI teams apply to real-world scenarios?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The study provides a categorization of human-AI team types, but it does not necessarily mean that all insights are applicable across these categories.
  • Further research could focus on validating the categorization scheme and its applicability in real-world settings.

Raw Abstract / Excerpt

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 combination of holistic team-level characteristics, indicating there are multiple disparate team types studied under the same definition. In turn, this raises the question of whether insights are truly transferable between papers. We conclude with guidance on how to identify the types of human-AI teams studied, a checklist for reporting a human-AI team in research work, and ways in which the field can be further synthesised.