Principled Design for Trustworthy AI - Interpretability, Robustness, and Safety across Modalities

ICLR 2026 Workshop

📍 International Conference on Learning Representations (ICLR 2026)
📅 Date: Sunday April 26 or Monday April 27 · 🌍 Location: Rio de Janeiro, Brazil

Overview

Modern AI systems, particularly large language models, vision-language models, and deep vision networks, are increasingly deployed in high-stakes settings such as healthcare, autonomous driving, and legal decisions. Yet, their lack of transparency, fragility to distributional shifts between train/test environments, and representation misalignment in emerging tasks and data/feature modalities raise serious concerns about their trustworthiness.

This workshop focuses on developing trustworthy AI systems by principled design: models that are interpretable, robust, and aligned across the full lifecycle – from training and evaluation to inference-time behavior and deployment. We aim to unify efforts across modalities (language, vision, audio, and time series) and across technical areas of trustworthiness spanning interpretability, robustness, uncertainty, and safety.

Call for Papers

We invite submissions on topics including (but not limited to):

Interpretable and Intervenable Models
- concept bottlenecks and modular architectures, mechanistic interpretability and concept-based reasoning, interpretability for control and real-time intervention;
Inference-Time Safety and Monitoring
- reasoning trace auditing in LLMs and VLMs, inference-time safeguards and safety mechanisms, chain-of-thought consistency and hallucination detection, real-time monitoring and failure intervention mechanisms;
Multimodal Trust Challenges
- grounding failures and cross-modal misalignment, safety in vision-language and deep vision systems, cross-modal alignment and robust multimodal reasoning, trust and uncertainty in video, audio, and time-series models
Robustness and Threat Models
- adversarial attacks and defenses, robustness to distributional, conceptual, and cascading shifts, formal verification methods and safety guarantees, robustness under streaming, online, or low-resource conditions;
Trust Evaluation and Responsible Deployment
- human-AI trust calibration, confidence estimation, uncertainty quantification, metrics for interpretability/alignment/robustness, transparent and accountable deployment pipelines, safety alignment;
Safety and Trustworthiness in LLM Agents
- safety and failures in planning and action execution, emergent behaviors in multi-agent interactions, intervention and control in agent loops, alignment of long-horizon goals with user intent, auditing and debugging LLM agents in real-world deployment.

Reviews are double-blind and the accepted papers are non-archival. Accepted papers will be presented as posters and/or short talks.

Submission Instruction

Format: (1) Short paper track: max 4 pages, excluding references; (2) Long paper track: max 9 pages, excluding references. Please use the LaTeX style files (ICLR conference style) provided here.
Submission: Openreview link
Submission deadline: Feb 2, 2026 (AoE)
Guideline: The content of submission needs to be original and not accepted in other archival venues by the time of our submission deadline. Violation of this policy will be desk-rejected.

Note that for Openreivew submission, new profiles created without an institutional email will go through a moderation process that can take up to two weeks. New profiles created with an institutional email will be activated automatically.