The 3rd Workshop on Regulatable ML @NeurIPS2025 on Regulatable ML Workshop

Call for Papers

Mon, 01 Jan 0001 00:00:00 +0000

Topics

We encourage paper submissions relevant to (but not limited to) the following topics:

Survey workflow-level defenses (e.g., sandboxing, provenance tracking) to prevent data leakage and unauthorized actions
Explore model evaluation protocols and compliance criteria inspired by the EU AI Act’s conformity assessments (risk assessments, technical documentation, logging) and investigate the role of independent third-party audits and “safety labels” for AI systems.
Analyze liability frameworks for harm caused by misaligned or compromised AI agents and evaluate the implications of cross-border coordination, drawing on cooperation insights on technical AI safety collaboration among geopolitical rivals

Call for Papers

Mon, 01 Jan 0001 00:00:00 +0000

Topics

The main focus of this workshop is about identifying and bridging the gaps between ML research and regulatory principles. We encourage paper submissions relevant to (but not limited to) the following topics:

Theoretical and/or empirical studies to highlight the operational gaps between existing regulations and SOTA ML research;
Evaluation and auditing frameworks for ensuring that ML models are in compliance with regulatory guidelines;
Theoretical and/or empirical studies to highlight tensions between different desiderata (e.g., fairness, explainability, privacy) of ML models outlined by various regulatory frameworks;
Novel algorithmic frameworks to operationalize the right to explanation, the right to privacy, the right to be forgotten, and to ensure fairness and robustness of ML models;
New regulation challenges posed by emerging large generative models and methods to mitigate them;
Perspective/position papers that outline open problems and negative results relevant to ML regulation, or flawed research and development practices that misalign with regulatory policies.

Important Dates

All deadlines are due 23:59 PM in GMT time zone.

Call for Papers

Mon, 01 Jan 0001 00:00:00 +0000

Topics

With the widespread deployment of machine learning, there is a growing concern about the ethical and legal implications of these technologies. Governments worldwide have responded by implementing regulatory policies to safeguard algorithmic decisions and data usage practices. However, there is still a considerable gap between current machine learning research and these regulatory policies. Translating these policies into algorithmic implementations is highly non-trivial, and there may be inherent tensions between different regulatory principles.