BabZituna scores how well you fit a job. We audit that scoring for bias and publish what we find — including what we haven't yet ruled out.
Internal audit · June 2026 · the first of a quarterly cadence.
The match score combines six inputs: your skills, experience, location, salary expectation, job type, and work style. It does not receive your name, photo, age, gender, nationality, or the prestige of your school or past employers — those fields are never passed to the scorer.
For each test we took one synthetic CV, changed a single feature, and measured the change in score (0–100). A gap above 0.5 points is treated as a flag.
Score is identical (0 pt gap) across all 40 names. The scorer never receives the candidate's name as an input, so this is a structural fairness property — not a statistical result. See methodology critique.
An identical CV against a London job spans 20 pts across candidate cities — Hill-curve distance decay (half-score at 65 km) drives the location dimension toward zero once a candidate is far from the role. The WASPAS weak-link blend would otherwise amplify that single weak dimension well beyond its 15% nominal weight (a ~34-pt swing); a PROPORTIONALITY CAP — a higher geometric floor on the location term (MATCH_GEOMETRIC_LOCATION_FLOOR, CTO decision 2026-06-19) — bounds the realized swing back toward location's ~15% weight (~20 pt). This remains a legitimate commute signal that advantages candidates who live near the role's city — measured here across ten world cities on four continents — the single real geographic disparity the audit surfaces, and remote-capable roles fully neutralise it (Slice 4).
We also measured each city under a GB country filter: the score was identical (max filter effect 0 pts). The cross-border geo-tier penalty (the 0.1x Tier-4 multiplier) keys off the JOB's country vs the candidate's country FILTER, not the candidate's own location — so a country filter does NOT compound the candidate-geography gap.
Mitigation candidate: cap the location dimension's downward pull for remote-capable roles so a distant candidate for a remote-friendly job isn't penalised on commute they'll never make (see methodology doc).
Score is identical (0 pt gap) across elite/mid/access universities. Education contributes a flat +5 for PRESENCE only; the school NAME is never scored. Structural property, not a statistical result.
For a REMOTE-CAPABLE job, an identical CV scores within 0 pt across all cities — the distance penalty is waived, so candidate geography no longer moves the score. The same CV against a HYBRID job still spans 20 pt (distance preserved by design — a hybrid hire commutes some days).
Policy: the work-style classifier (job.workStyle) is the authority; hybrid is deliberately excluded from the cap. On-site/unknown fall back to the legacy job.remote flag.
CI pins: remote-capable gap must stay <= 1 pt; hybrid must keep decaying (> 1 pt) so the cap can't silently swallow hybrid commute signal.
Found: the one real disparity was geographic — a candidate far from a job lost most of the location dimension, and the weak-link blend briefly amplified that to a 34-point swing across cities for an identical CV, sharper than location's 15% weight describes.
Changed: for fully-remote roles we waive the distance penalty entirely — where you live can't move your score for a job you'd do remotely. For on-site and hybrid roles we added a proportionality cap so the blend can't push geography's realized influence past roughly its declared 15% weight — distance still counts (a hybrid hire commutes some days), but in proportion, not amplified.
Re-measured: remote-capable jobs now vary by 0 points across cities (was 34); on-site/hybrid jobs vary by about 20 points — distance preserved, but capped to match location's published weight (down from the uncapped 34). Our test suite fails the build if either property breaks.
scripts/bias-audit/, docs/legal/bias-audit-2026-06.results.json) and the methodology in docs/legal/bias-audit-methodology.md. Questions: privacy@babzituna.com.