Can reviewers change the coding decision on documents they have previously reviewed in an Active Learning project?

In the context of an Active Learning project, reviewers can indeed change the coding decisions on documents they have previously reviewed. However, these changes are typically not reflected immediately but are instead considered for the next model build. This allows the machine learning model to learn from the updated coding decisions, improving its accuracy and performance in future iterations. The concept behind this approach is to refine the model continuously based on the most current and precise data available, taking into account any updates made by the reviewers.

This process ensures that the model benefits from ongoing input and corrections, thereby enhancing its capabilities in understanding and categorizing documents more effectively over time. Immediate changes to coding decisions, while beneficial in making real-time adjustments, would not contribute to the model's learning process until it is built anew, which is why the option indicating that changes are accepted for the next model build is correct.