Reviewed quarterly. Last reviewed 2026-05.
Hiring AI is high-risk under the EU AI Act, so the methodology behind every fairness check is public and citable. The live numbers stay private to each employer; the way we calculate them does not.
Every assessment outcome is tracked across protected groups using the four-fifths rule: if the selection rate for any group is less than four-fifths of the rate for the most-selected group, the platform flags the cluster for human review. The four-fifths rule is the canonical adverse-impact indicator referenced in 29 CFR § 1607.4 and consistently applied across UK employment-tribunal precedent.
Adverse-impact monitoring is the operational tool that lets employers evidence compliance with the indirect-discrimination provisions of the Equality Act 2010. KimonRecruit produces tribunal-grade evidence of every adverse-impact flag and the human override that resolved it, designed to support the employer's defence, not replace it.
Adverse-impact calculations are computed by the open-source Holistic AI library and cross-verified against Fairlearn for subgroup analysis. Methodology is open and citable; we do not roll our own.
The methodology described on this page is reviewed quarterly. Any change is logged with a date and the reviewing engineer's name. Substantive changes trigger a new methodology-version stamp; tenant audit trails record which methodology version was active at the time of every decision.
Live ratios and per-cluster figures are sensitive: they reveal information about specific tenants and specific candidate pools. We make the methodology public so it can be inspected; we keep the live numbers in each tenant's own dashboard so they remain auditable to the tenant, and, on lawful request, to the regulator, but never to the open web.
We re-read the methodology, and any new regulatory guidance, every three months. Anything material is captured here on this page.
Recruiter? Live adverse-impact numbers are in your dashboard, under each job’s Adverse impact tab.
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