STRAI
2nd International Workshop on Secure, Trustworthy, and Robust AI
Workshop Chairs
- → Georgios Spathoulas
- → Georgios Kavallieratos
- → Aida Akbarzadeh
- → Sebastian Heil
Detail 01/04
STRAI 2026 addresses the growing need for AI systems that are secure, trustworthy, resilient, and robust in the face of privacy risks, adversarial manipulation, and broader governance challenges. The workshop approaches AI trustworthiness from multiple perspectives, including policy and governance, ethics, human-AI collaboration, technical robustness, security and privacy, and resilience to evolving threats. It promotes interdisciplinary collaboration and invites contributions offering concrete methodologies, real-world applications, and policy-relevant insights into the complex challenges of trustworthy AI across critical domains.
Topics of interest include, but are not limited to 02/04
- Transparency, Explainability, and Interpretability of AI models
- Security and Privacy Issues in AI systems
- Trustworthy AI Systems and their evaluation metrics
- Evaluating Explainability and Trust in AI-enabled decision-making
- Misinformation Detection and defense mechanisms
- Data Poisoning and Adversarial Examples attacks and countermeasures
- Audit Techniques for data and AI models to ensure accountability
- Fairness and Exclusion Studies (benchmarks and datasets)
- Evaluation Methods to ensure fair outcomes, especially for underrepresented groups
- Social Good and Participatory AI and applications of the above principles to critical domains
- Robustness, Safety, and Security of AI systems
- Availability and Reliability of AI systems in critical applications
- AI, Surveillance, Privacy, Security, and Reliability - Balancing innovation and ethical considerations
- Governance, Regulation, Control, Safety, and Security of AI systems
- Trustworthiness and Decision Making in AI-enabled systems
- Accountability, Responsibility, and Trustworthiness of AI systems
- AI Applications to Promote Security and Robustness
- Human Oversight and Control in AI systems
- Human Trust and Understanding of AI - Building and maintaining trust in AI systems
- Human-Centered AI, Human-AI Interaction, and Human-AI Teaming - Enhancing collaboration and co-adaptation
- Human-Machine Trust and Risk - Understanding risk perception and trust calibration
- Performance Benchmarks for Trust in AI systems
- Public Perception and societal acceptance of trustworthy AI systems
- Case Studies and Simulation in the Energy, Manufacturing, Transportation, Health, Space, and other Industrial and Critical Infrastructure Sectors - Demonstrating real-world applications and implications of trustworthy AI
Workshop Chairs 03/04
Workshop Chairs
Program Committee
Submission 04/04
Submission Guidelines
The submission guidelines valid for the workshop are the same as for the ARES conference.