Fairness at Xobin starts by naming, in writing, the subgroups we are testing against and the fairness definition we are holding the assessment to. Group definitions are set by the customer's compliance framework — typically gender, age band, and region — and are used only in aggregate, never as an input to scoring an individual candidate. The scope is documented in the assessment's evidence dossier so a reviewer can see exactly what was audited and what was not.
For every scored item and every full assessment we run adverse-impact analysis against the standard 4/5ths rule, screen items for differential item functioning (DIF) — the case where candidates of equal underlying ability answer an item differently across groups — and track group-mean gaps over time. The results are reported alongside reliability and validity, because a fair test is a joint claim about all three.
Items that show consistent DIF are pulled from the active item pool and sent back to subject-matter experts for review, rewrite, or retirement. Assessments that fail the 4/5ths ratio in aggregate are flagged to the customer with a re-validation recommendation before continued use. Bias auditing is a loop, not a certificate — the same tests run again after every remediation.
The fairness checks described here are applied continuously to the assessments customers hire with. Teams who want the same rigour in their own hiring can use it directly through Xobin.
The methodology described here is what runs inside every Xobin assessment and AI interview — so hiring teams can make talent decisions on evidence, not instinct. See Xobin.