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AI-generated articles published under your name raise critical questions about responsibility and correction. Different actions focus on fact-checking, systematic error handling, verifying facts, and confirming historical details, each leading to distinct ways to protect your reputation and maintain accuracy. Understanding these actions helps you decide how to disclose AI use, correct errors, and respond to challenges in published work.

The replication crisis has exposed how fragile published findings can be when studies are repeated under closer scrutiny. This overview explains the roles of preregistration, statistical thresholds, trial transparency, and professional incentives in shaping research reliability. Readers will understand the main reform proposals, their tradeoffs, and why the debate over whether science can correct itself remains unresolved.