Call for Papers

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.

The main focus of this workshop is to identify and bridge 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 that highlight the operational gaps between existing regulations and SOTA ML research;
  • Evaluation and auditing frameworks for ensuring that ML models comply 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;
  • 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;
  • New regulation challenges posed by large generative models and methods to mitigate them, especially in the area of creative industries;
  • Regulation needs for preventing catastrophic risks brought by artificial general intelligence (AGI).

Important Dates

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

  • Abstract Deadline: Aug 25 Sep 7, 2024
  • Submission Deadline: Aug 30 Sep 12, 2024
  • Acceptance Notification: Oct 14, 2024

Submission Instruction

This workshop is non-archival. The review process is double-blinded. Submissions should be anonymized appropriately.

Abstracts and papers can be submitted through OpenReview.

Format

We welcome both short papers no longer than 4 pages and long papers of up to 9 pages, excluding references and unlimited supplementary materials. Please use the RegML @ NeurIPS 2024 template and submit your paper(s) in PDF format.