ICML 2025 Past Fairness & ethics
ICML Workshop on Technical AI Governance (TAIG)
ICML 2025 Workshop TAIG
- Submission deadline
- May 13, 2025, 11:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (45)
Fetched from OpenReview (v2) on 2026-06-10.
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A Blueprint for a Secure EU AI Audit Ecosystem
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A Conceptual Framework for AI Capability Evaluations
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A Taxonomy for Design and Evaluation of Prompt-Based Natural Language Explanations
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Acceleration potential in the GPU design-to-manufacturing pipeline
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Access Controls Will Solve the Dual-Use Dilemma
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AI Benchmarks: Interdisciplinary Issues and Policy Considerations
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Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments
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CALMA: Context‑Aligned Axes for Language Model Alignment
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Compute Requirements for Algorithmic Innovation in Frontier AI Models
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Deprecating Benchmarks: Criteria and Framework
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Detecting Compute Structuring in AI Governance is likely feasible
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Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape
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Evaluating LLM Agent Adherence to Hierarchical Principles: A Lightweight Benchmark for Verifying AI Safety Plan Components
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Expert Survey: Technical AI Safety & Security Research Priorities
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Exploring an Agenda on Memorization-based Copyright Verification
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Exploring Functional Similarities of Backdoored Models
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ExpProof : Operationalizing Explanations for Confidential Models with ZKPs
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Fallacies of Data Transparency: Rethinking Nutrition Facts for AI
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Fragile by Design: Formalizing Watermarking Tradeoffs via Paraphrasing
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From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms
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Guaranteeable Memory: An HBM-Based Chiplet for Verifiable AI Workloads
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Hardware-Enabled Mechanisms for Verifying Responsible AI Development
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In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI
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LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries
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LLMs Can Covertly Sandbag On Capability Evaluations Against Chain-of-Thought Monitoring
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Locking Open Weight Models with Spectral Deformation
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Marginal Risk Relative to What? Distinguishing Baselines in AI Risk Management
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Measuring What Matters: A Framework for Evaluating Safety Risks in Real-World LLM Applications
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Meek Models Shall Inherit The Earth
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Methodological Challenges in Agentic Evaluations of AI Systems
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Position: Formal Methods are the Principled Foundation of Safe AI
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Position: Generative AI Regulation Can Learn from Social Media Regulation
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Practical Principles for AI Cost and Compute Accounting
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Probing Evaluation Awareness of Language Models
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Proofs of Autonomy: Scalable and Practical Verification of AI Autonomy
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Relative Bias: A Comparative Approach for Quantifying Bias in LLMs
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Reproducibility: The New Frontier in AI Governance
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Robust ML Auditing using Prior Knowledge
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Scaling Limits to AI Chip Production
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Societal Capacity Assessment Framework: Measuring Advanced AI Implications for Vulnerability, Resilience, and Transformation
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Technical Requirements for Halting Dangerous AI Activities
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The Strong, weak and benign Goodhart's law. An independence-free and paradigm-agnostic formalisation
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Trends in AI Supercomputers
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Trends in Frontier AI Model Count: A Forecast to 2028
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Watermarking Without Standards Is Not AI Governance