# Human-Centered Human-AI Collaboration (HCHAC)

arXiv: https://arxiv.org/abs/2505.22477 · HTML: https://mlsanity.com/paper/2505.22477/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 136 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 17 of 17)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [When combinations of humans and AI are useful: A systematic review and meta-analysis](https://mlsanity.com/paper/2405.06087/) (2024)
- ["Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction](https://mlsanity.com/paper/2210.03735/) (2022)
- [Cooperating with Machines](https://mlsanity.com/paper/1703.06207/) (2017)
- [The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems](https://mlsanity.com/paper/2105.03354/) (2021)
- [Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review](https://mlsanity.com/paper/2310.19778/) (2023)
- [Explainable AI improves task performance in human-AI collaboration](https://mlsanity.com/paper/2406.08271/) (2024)
- [Value Compass Benchmarks: A Platform for Fundamental and Validated Evaluation of LLMs Values](https://mlsanity.com/paper/2501.07071/) (2025)
- [Levels of AI Agents: from Rules to Large Language Models](https://mlsanity.com/paper/2405.06643/) (2024)
- [Evaluating and Modeling Social Intelligence: A Comparative Study of Human and AI Capabilities](https://mlsanity.com/paper/2405.11841/) (2024)
- [Human-AI interaction: An emerging interdisciplinary domain for enabling human-centered AI](https://mlsanity.com/paper/2112.01920/) (2021)
- [Agent Teaming Situation Awareness (ATSA): A Situation Awareness Framework for Human-AI Teaming](https://mlsanity.com/paper/2308.16785/) (2023)
- [New research paradigms and agenda of human factors science in the intelligence era](https://mlsanity.com/paper/2208.12396/) (2022)
- [Applying HCAI in developing effective human-AI teaming: A perspective from human-AI joint cognitive systems](https://mlsanity.com/paper/2307.03913/) (2023)

## Abstract (arXiv)

In the intelligent era, the interaction between humans and intelligent systems fundamentally involves collaboration with autonomous intelligent agents. Human-AI Collaboration (HAC) represents a novel type of human-machine relationship facilitated by autonomous intelligent machines equipped with AI technologies. In this paradigm, AI agents serve not only as auxiliary tools but also as active teammates, partnering with humans to accomplish tasks collaboratively. Human-centered AI (HCAI) emphasizes that humans play critical leadership roles in the collaboration. This human-led collaboration imparts new dimensions to the human-machine relationship, necessitating innovative research perspectives, paradigms, and agenda to address the unique challenges posed by HAC. This chapter delves into the essence of HAC from the human-centered perspective, outlining its core concepts and distinguishing features. It reviews the current research methodologies and research agenda within the HAC field from the HCAI perspective, highlighting advancements and ongoing studies. Furthermore, a framework for human-centered HAC (HCHAC) is proposed by integrating these reviews and analyses. A case study of HAC in the context of autonomous vehicles is provided, illustrating practical applications and the synergistic interactions between humans and AI agents. Finally, it identifies potential future research directions aimed at enhancing the effectiveness, reliability, and ethical integration of human-centered HAC systems in diverse domains.