Anonymize Code for Submissions

Post LinkedIn

🤖Read original on Reddit r/MachineLearning

#code-anonymization #submissions #blind-reviewgithubgithub

💡Pro tip: Anon GitHub accounts for ML paper code—avoid review biases

⚡ 30-Second TL;DR

What Changed

Anonymize GitHub repo for blind review

Why It Matters

Standardizes best practices for ethical double-blind submissions in ML research.

What To Do Next

Create a new anonymous GitHub repo today and test uploading your code without personal identifiers.

Who should care:Researchers & Academics

Key Points

•Anonymize GitHub repo for blind review
•Use throwaway anonymous account
•Relocate code post-acceptance

🧠 Deep Insight

AI-generated analysis for this event.

🔑 Enhanced Key Takeaways

•Major AI conferences like NeurIPS and ICML now explicitly mandate the use of anonymization services like 'Anonymous GitHub' or 'OpenReview' to prevent metadata leakage (e.g., commit history, user profile links) that could deanonymize authors.
•Standard practice now includes scrubbing sensitive metadata from Git history using tools like BFG Repo-Cleaner or git-filter-repo, as simple file deletion does not remove author identity from the commit logs.
•Reviewers are increasingly instructed to report 'active' deanonymization attempts, such as linking to personal websites or social media within the code comments or README files, which can lead to desk rejection.

🔮 Future ImplicationsAI analysis grounded in cited sources

Automated anonymization pipelines will become standard in conference submission portals.

Manual scrubbing is prone to human error, leading to frequent accidental deanonymization, which necessitates a shift toward platform-level automated sanitization.

Git commit history will be treated as PII (Personally Identifiable Information) in academic submissions.

As AI conferences tighten double-blind policies, the historical metadata embedded in Git repositories is increasingly viewed as a violation of anonymity protocols.