# What Types of Human-AI Teams Exist?

arXiv: https://arxiv.org/abs/2607.02198 · HTML: https://mlsanity.com/paper/2607.02198/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 74 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Builds on (most cited first, 18 of 18)

- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence](https://mlsanity.com/paper/2404.00029/) (2024)
- [On the Utility of Prediction Sets in Human-AI Teams](https://mlsanity.com/paper/2205.01411/) (2022)
- [Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task](https://mlsanity.com/paper/2409.08811/) (2024)
- [Who/What is My Teammate? Team Composition Considerations in Human-AI Teaming](https://mlsanity.com/paper/2105.11000/) (2021)
- [A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams](https://mlsanity.com/paper/2401.14432/) (2024)
- [The BIG Argument for AI Safety Cases](https://mlsanity.com/paper/2503.11705/) (2025)
- [Towards Visual Explainable Active Learning for Zero-Shot Classification](https://mlsanity.com/paper/2108.06730/) (2021)
- [Collaborative human-AI trust (CHAI-T): A process framework for active management of trust in human-AI collaboration](https://mlsanity.com/paper/2404.01615/) (2024)
- [Comparing Zealous and Restrained AI Recommendations in a Real-World Human-AI Collaboration Task](https://mlsanity.com/paper/2410.11860/) (2024)
- [A Case for Backward Compatibility for Human-AI Teams](https://mlsanity.com/paper/1906.01148/) (2019)
- [Artifact magnification on deepfake videos increases human detection and subjective confidence](https://mlsanity.com/paper/2304.04733/) (2023)
- [Unraveling Human-AI Teaming: A Review and Outlook](https://mlsanity.com/paper/2504.05755/) (2025)
- [Unravelling Responsibility for AI](https://mlsanity.com/paper/2308.02608/) (2023)
- [Agent Teaming Situation Awareness (ATSA): A Situation Awareness Framework for Human-AI Teaming](https://mlsanity.com/paper/2308.16785/) (2023)
- [Modeling Human-AI Team Decision Making](https://mlsanity.com/paper/2201.02759/) (2022)
- [Incorporating Human Flexibility through Reward Preferences in Human-AI Teaming](https://mlsanity.com/paper/2312.14292/) (2023)
- [Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning](https://mlsanity.com/paper/2312.15160/) (2023)

## Built on by (most cited first, 1 of 1)

- [Working with Agentic `Teammates': When a New Organizational Actor Collides with the Human Ecosystem of Work](https://mlsanity.com/paper/2609.29901/) (2026)

## Abstract (arXiv)

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.