What is the recommended number of concurrent reviewers for an active learning project?

In active learning projects, the recommended number of concurrent reviewers is typically higher due to the nature of the task, which often involves large datasets that require efficient labeling and feedback. Having 150 or fewer concurrent reviewers allows for sufficient manpower to process data rapidly, enhancing the learning model's accuracy by incorporating diverse perspectives on the data.

Utilizing a larger group of reviewers can lead to improved data quality as different reviewers may identify unique insights or nuances in the data that others may miss. This breadth of review helps in better generalizing the model to work effectively across varied contexts within the dataset. Therefore, maintaining a cap at 150 ensures that the project is manageable while still harnessing the benefits of collaboration and collective input from a notable size of reviewers.