IEEE Workshop on Trustworthy and Privacy-Preserving Human-AI Collaboration

This workshop explores the evolving relationship between humans and AI systems, with a focus on fostering trustworthy and privacy-preserving collaboration. As AI capabilities grow and its presence in daily life expands, it is essential that these systems align with human values to remain responsible, effective, and secure. Although human-AI collaboration offers significant potential for enhanced decision-making and societal benefit, it also raises critical challenges, such as privacy risks, trust and safety concerns, and cybersecurity threats across diverse domains.

Our goal is to foster interdisciplinary dialogue and shape a roadmap for effective and trustworthy human-AI collaboration. We invite contributions that bridge the gap between machine intelligence and human understanding, particularly in shared decision-making scenarios. The workshop promotes the development of adaptive, hybrid, and emerging AI systems that respond to dynamic contexts while respecting human agency and enhancing human capabilities. We welcome insights from user studies and the design of collaborative frameworks that strengthen trust, transparency, privacy, and security. We also encourage discussions addressing key questions such as: What methods and metrics are needed to evaluate human-AI teams effectively? What factors influence trust, performance, and responsible AI deployment?

Topics of interest include, but are not limited to:

• Human-AI collaborative paradigms across domains, such as transportation, healthcare, manufacturing, and education.

• Fairness, transparency, ethics, and accessibility in AI

• Trust, privacy, and security in AI

• Cognitive, affective, and social aspects of safe Human-AI collaboration 

• Methods and metrics for assessing human-AI teamwork and its trustworthiness 

• Multi-modal sensing and social signal processing in human-AI interaction to enhance trust and safety

• Trusted Human-centered/interactive machine learning 

• Approaches for privacy-, security-, and trustworthy-by-design human-AI collaboration and teaming