How AI Code Reviewers Develop a 'Rubber Stamp' Problem and How to Fix It

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AI code review tools can gradually become overly lenient, consistently approving pull requests with little meaningful feedback — a pattern dubbed the 'rubber stamp effect.' This happens due to training data skewed toward already-approved code, feedback loops that treat low-risk files as safe, and teams prioritizing review speed over defect detection. The result is false confidence among developers, allowing security flaws, logic errors, and architectural issues to slip through unnoticed. Warning signs include approval rates above 90%, repetitive generic comments, and developer complaints about bugs discovered after merging. Experts recommend fixes such as injecting known-bad code during model evaluation, adding confidence thresholds that escalate uncertain reviews to humans, and retraining models using rejected pull requests and security advisories.

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