How often are bias and model quality tests conducted?

RChilli treats fairness and quality testing as a continuous lifecycle activity, not a one-time exercise.

During development and model design

Sensitive and non-job-relevant features are removed or restricted early. Initial bias testing is performed using synthetic datasets and controlled counterfactual cases.

Pre-deployment

Before any model or feature is released, RChilli runs validation covering bias testing, matching accuracy, and ranking consistency. Outputs are compared against previous stable versions and expected benchmark behavior to catch regressions before release.

Post-deployment

Once live, system behavior is continuously monitored through API performance tracking, live output reviews, and detection of unusual scoring or ranking shifts.

Periodic audits

Scheduled reviews are conducted over time to re-run bias tests on updated datasets, review long-term trends, and detect drift caused by system updates, changing data patterns, or job market shifts.

This layered frequency helps maintain fairness, performance, and stability over time.

If you need further assistance, feel free to contact the RChilli Support Team by sending an email to support@rchilli.com.