Bridging AI Safety, Social Good, and Governance
Reviewers: We will have several Top Reviewer awards with incentives!
We will also later open a channel for travel funding applications for under-represented groups.
We offer a Top Paper Prize in the field of Cooperative AI and travel support for under-represented groups in that field, both sponsored by CAIF.
We're open for sponsors! Please email zjingchen@cs.toronto.edu directly!
Special Theme: Cooperative AI + AI for Civic Discourse
Agentic AI systems increasingly shape how billions of people engage with public institutions, civic discourse, and society at large. While much work has focused on making models safer in avoiding harmful output, it is equally important for these improvements to translate into social good at scale.
The AI4GOOD workshop brings together the AI safety, AI for social good, and AI policy/governance communities to connect what models can do as individual systems with what they do when deployed across populations. We aim to bridge technical advances in trustworthy AI with real-world societal impact, including protecting democratic institutions and civic discourse.
We welcome submissions in a wide range of topics (if you're not sure about your paper, we encourage you to just submit!).
Evaluation, auditing, and red-teaming of models for harmful behaviors and failure modes; safety monitoring after deployment; alignment and robustness methods.
Methods, evidence standards, and evaluation frameworks for demonstrating real-world benefit and avoiding unintended harms at population scale.
Detection and mitigation of disinformation, manipulation, and influence operations; building resilience of the information ecosystem.
Accountable use of AI in government and civic settings, including transparency, documentation, procurement, and oversight practices.
AI systems that support public deliberation and civic engagement while preserving legitimacy and avoiding undue influence.
Multi-agent coordination, negotiation, and conflict resolution; mechanisms for trust, commitment, and cooperation among AI systems and between AI and humans.
Awarded to the best paper in the Cooperative AI special theme. Sponsored by the Cooperative AI Foundation (CAIF).
Recognition with incentives for the most thorough and constructive reviewers.
Travel support for authors with accepted papers on the Cooperative AI theme. Sponsored by CAIF.
A channel for travel funding applications for under-represented groups will open later. Stay tuned!
Format: Papers should be 2–8 pages (excluding references and appendices) using the ICML 2026 style.
Review Process: All submissions will be reviewed double-blind via OpenReview. Please ensure your submission is fully anonymized.
Non-Archival: Work may be submitted to or published at other venues.
We encourage you to submit anyway! It is possible that we may have to desk-reject some papers that we believe might be a better fit for other venues. This will not be made public, so don't worry—it does not represent any judgment of your work, just an administrative necessity as our review capacity is limited.
On our side, we are open to any submissions currently under review (at ICML or other venues). However, it is your responsibility to make sure that the other venue is OK with your submission to our workshop. For example, CVPR considers peer-reviewed workshop contributions with more than 4 pages as published, even if the workshop is non-archival (which we are). Similarly, there are some case-by-case rules for journals, where you may want to check with the editors.
We are also open to that from our side, especially if you feel like your work spans the interest of several workshops. Similarly, you may want to check the website of other workshops to make sure they are OK with it as well.
We are very flexible with presenters and plan to accommodate a hybrid modality. That said, we strongly encourage in-person presence, and all contributed talks (i.e., oral papers) are required to be presented in person.
All deadlines are 11:59 PM Anywhere on Earth (AoE).
Vector Institute
University of Toronto
University of Michigan
Harvard University
University of Toronto & Schwartz Reisman Institute
University of Michigan
ETH Zürich & University of Zürich
ETH Zürich & MPI-IS
Carnegie Mellon University
University of Michigan
University of Pennsylvania
EuroSafeAI
For questions and sponsorship, feel free to contact us at zjingchen@cs.toronto.edu.
