AI/ML data labeling app.
Guideline 3.2.2 - Business - Other Business Model Issues - Unacceptable
The primary purpose of your app is to encourage users to watch ads or perform marketing-oriented tasks, which is not appropriate for the App Store.
Next Steps
We encourage you to review your app concept and incorporate different content and features that are in compliance with the App Store Review Guidelines.
We'll submit a new version for your consideration, removing placeholder default text and addressing Guideline 3.2.2: "users to watch ads or perform marketing-oriented tasks."
This is a data labeling app, used by data scientists to improve their CoreML and other machine learning models. It is not marketing nor an ad serving app, but rather a convenient method for skilled users to be compensated for helping to improve the quality of machine learning models. We're paying for skill and talent, similar to how you allow game developers to reward users for their gaming skill.
https://cloud.google.com/data-labeling/docs/instructions
Designing good instructions
Good instructions are the most important factor in getting good human labeling results. Since you know your use case best, you need to let the human labelers know what you want them to do. Here are some guidelines for creating good instructions:
Remember that the human labelers don't have your domain knowledge. The distinctions you ask labelers to make should be easy to understand for someone unfamiliar with your use case. Don't make the instructions too long. It's best if an labeler can review and understand them within 20 minutes. Instructions should describe the concept of the task as well as details about how to label the data. For example, for a bounding box task, describe how you want labelers to draw the bounding box. Should it be a tight box or loose box? If there are multiple instances of the object should they draw a big bounding box or multiple smaller ones? Your instructions should cover all labels in the annotation specification set. The label name in the instructions should match the name in the annotation specification set. Include both positive and negative examples for each potential situation. Describe all edge cases that you can think of. For example, should the labeler label an image as "dog" if it shows only part of the dog? It often takes several iterations to create good instructions. We recommend having a small dataset labeled first, then adjust your instructions based on what you see in the results you get back.