Toward Human-Centered Human-AI Interaction: Advances in Theoretical Frameworks and Practice
Zaifeng Gao, Yuanxiu Zhao, Hanxi Pan, Wei Xu

TL;DR
This paper reviews a decade of research on human-centered AI interaction, proposing frameworks and methods to enhance collaboration, explainability, and trust in AI systems across various domains.
Contribution
It introduces a comprehensive interdisciplinary framework for human-AI interaction, including theoretical models, hierarchical methodologies, and empirical validation in real-world applications.
Findings
Validated frameworks in autonomous driving and aircraft cockpits
Demonstrated improved human-AI collaboration and trust
Established a taxonomy of HCAI implementation methods
Abstract
With the rapid development of artificial intelligence (AI), machines are increasingly evolving into intelligent agents, and the human-machine relationship is shifting from traditional "human-computer interaction" toward a new paradigm of "human-AI collaboration." However, technology-centered approaches to AI development have gradually revealed limitations such as fragility, bias, and low explainability, highlighting the urgent need for human-centered AI (HCAI) design philosophy. As a systems engineering approach, the successful implementation of HCAI depends critically on the design and optimization of high-quality human-AI interaction (HAII). This paper systematically reviews our research team's nearly decade-long exploration and practice in HCAI. At the level of research vision, we were among the first in China to systematically propose HAII as an interdisciplinary field and to…
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Taxonomy
TopicsHuman-Automation Interaction and Safety · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI)
