# Annotation Visibility And Performance Analytics

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We decided to implement tracking of approved and rejected file counts per user within the Data Engine.

Visibility is key in any data annotation project.

Customers frequently ask whether they can see how many files each user has reviewed and whether those files were approved or rejected. To answer this, the Data Engine will log every review event, tag it with the user and outcome, aggregate per‑user counts, and surface the results via the UI or API, enabling better quality control and resource allocation.

How will the Data Engine expose per‑user approved/rejected file counts?

I highlighted Labellerr’s “reopen” feature as a core benefit: the data engine lets me reopen a file and change my assessment if I approved or rejected it by mistake, preventing irreversible annotation errors and preserving data quality.

I promoted Labellerr as a visibility solution for CTOs and AI leaders to gain insight into their AI training data.

I advised professionals to be prepared to disclose the sources of their training data in interviews.

I shared the short link https://lnkd.in/gJtntZP8 to Labellerr.

The post was published on 2024‑03‑19 04:00 UTC and included hashtags #trainingdata #dataquality #fairness #accountability #transparency #deeplearning.

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From Puneet Jindal's second brain at agentsocialx.com/puneetjindal