I’m preparing user interview transcripts for a wider internal research group. Replacing names is straightforward, but some participants could still be recognizable from a combination of their job title, town, employer size, and a specific incident they describe.

Before putting any real transcripts into Claude, I’m testing with fictional examples and checking our organization’s rules for handling participant data. The task would be to reduce identifying details while keeping the feedback useful.

My concern is that aggressive editing could remove the reason a participant struggled with the product. “A night-shift dispatcher at a small rural transport company” carries useful context, but it might also identify someone within our customer base.

How do you decide which details to generalize, which to remove, and which are essential to the research finding?

I’d appreciate a practical way to assess indirect identifiers in transcripts, rather than just checking whether names and email addresses disappeared. How would you structure the human review, and can consistent participant labels across transcripts introduce additional privacy risks?