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

Co-located with IEEE International Conference on CIC/TPS/CogMI

November 11th 2025 in Pittsburgh, PA

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

Tentative schedule

09:45 AM – 10:00 AM Welcome and Opening Remarks

10:00 AM – 10:15 AM Coffee Break & Informal Networking

10:15 AM – 11:00 AM Keynote Talk

11:00 AM – 12:15 PM Panel: Trustworthy- and privacy-by-design Human-AI collaboration Systems

12:15 PM – 1:30 PM Lunch Break

01:30 PM – 03:30 PM Paper Presentations – Session 1

03:30 PM – 03:45 PM Coffee Break & Informal Networking

03:45 PM – 04:45 PM Paper Presentations – Session 2

04:45 PM – 05:00 PM Closing Remarks and Adjournment

Keynote speaker

Vehicle Computing: A New Computing Paradigm in the Era of Autonomous Driving

Abstract: Vehicles have been used mainly for transportation in the last century. With the proliferation of onboard computing and communication capabilities, we envision that future connected and autonomous vehicles (CAVs) will serve as a mobile computing platform in addition to their conventional transportation role for the next century. In this presentation, Dr. Shi will present the vision of Vehicle Computing, a new era for the automotive industry, followed by two vital enabling technologies: autonomous driving and edge computing. Finally, he will talk about the recent development of D-STAR, a live and evolving testbed for vehicle computing on the STAR campus at the University of Delaware.

Biography: Dr. Weisong Shi is an Alumni Distinguished Professor and Department Chair of Computer and Information Sciences at the University of Delaware (UD). He leads the Connected and Autonomous Research (CAR) Laboratory. Dr. Shi is the Honorary Center Director of a recently funded NSF eCAT Industry-University Cooperative Research Center (IUCRC) (2023-2028), focusing on Electric, Connected, and Autonomous Technology for Mobility. He is an internationally renowned expert in edge computing, autonomous driving, and connected health. His pioneer paper, “Edge Computing: Vision and Challenges,” has been cited over 9300 times. He is the Editor-in-Chief of IEEE Internet Computing Magazine and the founding steering committee chair of several conferences, including the ACM/IEEE Symposium on Edge Computing (SEC), IEEE/ACM International Conference on Connected Health (CHASE), and IEEE International Conference on Mobility (MOST). He is a fellow of IEEE and a member of CRA’s Computing Community Consortium (CCC) Council.

Panel: Trustworthy- and privacy-by-design Human-AI collaboration Systems

Panelists:

Qiaoning Zhang, Arizona State University

Sauvik Das, Carnegie Mellon University

Jason Hong, Carnegie Mellon University

Shandong Wu, University of Pittsburgh

Submission

Workshop papers should follow the same submission guidelines and instructions for the main conference (IEEE TPS). The paper should not exceed 10 pages, including references. The standard IEEE conference paper format should be used. The IEEE two-column conference template can be downloaded from here. For questions, please contact the workshop organizers.

Submit your paper through EasyChair and select the "IEEE Workshop on Trustworthy and Privacy-Preserving Human-AI Collaboration" Track.

Important Dates

Submission deadline: Sept. 8, 2025 (Sept 30, 2025)

Acceptance notification: Sep 30, 2025 (Oct 14, 2025)

Final version due: Oct 20, 2025

Technical Program Committee Chairs

Co-chair: Na Du, University of Pittsburgh, na.du@pitt.edu

Co-chair: James B. D. Joshi, University of Pittsburgh, jjoshi@pitt.edu

Co-chair: Danda B. Rawat, Howard University, danda.rawat@howard.edu

Technical Program Committee Members

Imtiaz Ahmed, Howard University

Shih-Yi Chien, National Sun Yat-sen University

Yiheng Feng, Purdue University

Bimal Ghimire, Penn State University

Helge Janicke, Edith Cowan University

Muslum Ozgur Ozmen, Arizona State University

Houbing Herbert Song, University of Maryland, Baltimore County

Yuba Siwakoti, Central Washington University

Pingbo Tang, Carnegie Mellon University

Shandong Wu, University of Pittsburgh

Qiaoning Zhang, Arizona State University

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