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We already have a number of machine learning model and participant verification steps in place that allow us to screen out obvious bad actors, but we’re excited to introduce some new researcher-facing tools so you can better assess candidate quality, especially in cases with a bit more gray area.
We've added new quality signals to offer more insight on suspicious participant behavior. This includes activity data such as seeing when a participant joined, what time they applied to your study, previous applications & reviews, and more. We hope these updates give you that extra insight to confidently approve the best candidates for your studies.
You’ll see these updates on a participant’s profile—here’s what’s new:
For information on the statistics in the activity bar, you can hover over the question mark icons for a more detailed summary.
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