We say the AI is auditable — deliberately not "unbiased." No vendor can honestly promise unbiased screening; what we can promise is the evidence to check for yourself: every AI decision logged, an exportable decision history, and selection-rate monitoring built for the standard regulatory tests.
"Unbiased" is an outcome claim nobody can prove. "Auditable" is a process claim we can back up:
If a bias question ever comes up, you're not asking us to vouch for the AI — you're pulling the records.
Each row is one AI decision:
| Column | What it contains |
|---|---|
| Decision ID and type | Screening or interview analysis |
| Date | When the decision was made |
| Candidate | Initials only — the export is privacy-safe by design (no emails, phones, or profile links) |
| AI score and outcome | What the AI concluded |
| Hard disqualifiers | Any knockout criteria that fired |
| Human reviewed | Whether and when a person reviewed the decision |
| Role | The role the decision related to |
Beyond the raw log, PlacementFlow monitors pass-through rates — the share of candidates advancing at each AI-touched stage — compared across groups such as location bands, seniority bands, and job titles. That's the data you need to apply the four-fifths (80%) rule, the standard regulatory benchmark: if one group's selection rate falls below 80% of the highest group's rate, that's a flag worth investigating.
Two more guardrails run underneath:
Vendors that promise "unbiased AI" are making a claim they can't defend — and one that regulators and courts increasingly test. Our position: the AI does the operational work, a human approves high-stakes actions, and everything is logged so you can verify rather than trust. When a client's procurement team asks "how do you monitor your AI for bias?", the honest answer is a CSV, not an adjective.