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This study analyzes 53 papers on human-AI teams, categorizing them into five distinct clusters鈥擜I Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity鈥攂ased on psychological taxonomies of teaming. By highlighting the unique characteristics of each cluster, the research reveals significant variability in how human-AI teams are defined and studied, raising concerns about the transferability of insights across different studies. The authors provide a checklist for reporting human-AI team types and suggest pathways for synthesizing research in this evolving field.
The study uncovers five distinct types of human-AI teams, challenging the assumption that insights from one context can be easily applied to another.
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.
