Fairness Isn't a One-Time Test

Teams often treat fairness testing like a launch gate: test once, pass, ship, done. But an AI system that was fair on launch day can become unfair six months later — without anyone changing a line of code.

The world drifts. Customer demographics shift. Economic conditions change who applies. The data the system sees in production slowly stops resembling the data it was tested on — and the fairness results drift with it. In machine learning this is called drift; in plain English, it's the system aging out of its own test results.

In plain English: fairness has an expiration date.

The fix is scheduled re-testing: pick a cadence — quarterly is a sane default — re-run your fairness checks on recent production data, and compare against your launch baseline. Set a threshold in advance for what gap triggers action, so the decision isn't made in the heat of a bad result.

Put it on the calendar like any other control. In compliance, we don't audit once and retire; we audit on a cycle because conditions change. AI fairness deserves the same rhythm.

Test at launch. Then test again. That's the job.

I explain frameworks; I don't give legal counsel.