Hybrid (In-Person & Virtual)

ACM TRUST 2027 brings together researchers, practitioners, policymakers, educators, and system builders to advance the development and responsible use of trustworthy AI and computing systems. The conference focuses on secure, resilient, explainable, fair, and responsible approaches for designing, evaluating, and deploying AI-enabled technologies across high-impact domains. TRUST 2027 will be held in a hybrid format, offering both in-person attendance in Washington, DC and full virtual participation for authors and attendees who are unable to travel.

The conference provides a forum for presenting research, exchanging practical knowledge, discussing emerging challenges, and strengthening collaboration among communities working on trustworthy and responsible AI and computing systems.

TRUST 2027 welcomes contributions across the following research areas.

P1 Foundations of Trustworthy AI
- Formal verification of AI and ML systems
- Robustness, generalization, and adversarial resilience
- Interpretability and explainability foundations
- Uncertainty quantification and reliability metrics
- Causal reasoning and trustworthy inference
- Neuro-symbolic and hybrid AI approaches

P2 Secure and Resilient AI Systems
- Adversarial machine learning
- Data poisoning and model backdoor defenses
- Secure model deployment pipelines
- Privacy-preserving AI and federated learning
- AI supply chain security
- Runtime monitoring and anomaly detection

P3 Trustworthy AI in Systems and Infrastructure
- AI in cyber-physical systems
- Edge AI and trustworthy IoT
- Cloud and edge orchestration for safe AI
- AI reliability in distributed systems
- Trustworthy autonomous systems
- AI lifecycle management and MLOps assurance

P4 Responsible AI, Governance and Compliance
- Fairness, bias mitigation, and accountability
- Transparency and auditability
- AI risk management frameworks
- Standards and certification of AI systems
- Ethical system design methodologies
- Benchmarking responsible AI practices

P5 Evaluation, Benchmarking and Assurance
- Trustworthiness benchmarks
- Stress-testing frameworks
- Reproducibility and replicability in AI research
- Dataset integrity and data governance
- Risk assessment methodologies
- System-level validation and verification pipelines

P6 Domain-Specific Trustworthy Applications
- Healthcare AI safety
- Smart grid and critical infrastructure
- Industrial AI and cyber-physical systems
- Financial AI risk control
- Public-sector AI systems
- Climate and sustainability systems