SourceReddit r/MachineLearning•Stalecollected in 3h
Anonymize Code for Submissions
#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 — not the original article.
🔑 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.
⏳ Timeline
2018-05
Anonymous GitHub launched to provide a platform for double-blind peer review of code.
2020-06
NeurIPS formalizes the 'Code Submission Policy' requiring anonymized repositories for blind review.
2023-09
ICML updates submission guidelines to explicitly warn against metadata leakage in Git commit logs.
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Original source: Reddit r/MachineLearning ↗
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