Greenhouse
End-to-end applicant tracking system and structured-hiring platform, now layered with 'Greenhouse AI' features such as resume parsing, Talent Matching, and interview summarisation.
§ 01 — Score breakdown
§ Score breakdown
Category scoring
Weighted contribution shown to the right of each bar.
- 01
Article 11 Technical Documentation
Weight 20%78
+15.6
- 02
Bias Audit Transparency
Weight 18%86
+15.5
- 03
FRIA Support
Weight 15%42
+6.3
- 04
Data Governance Disclosure
Weight 15%64
+9.6
- 05
Human Oversight Design
Weight 12%66
+7.9
- 06
Post-Market Monitoring
Weight 12%58
+7.0
- 07
Customer Documentation
Weight 8%64
+5.1
§ 02 — Strongest · weakest
Strongest category
Article 11 Technical Documentation
Raw score 78 · contributes 15.6 to total.
Weakest category
Customer Documentation
Raw score 64 · contributes 5.1 to total.
§ 03 — Cited evidence
§ Evidence
Cited per category
Every score is backed by at least one cited piece of evidence.
§ 04 — Editorial notes
Company overview
Greenhouse Software, founded in 2012 and headquartered in New York City, is one of the most established applicant tracking and structured-hiring platforms. Historically it positioned itself around "structured hiring" and human judgment rather than algorithmic decision-making, and that posture is now codified in a public AI Principles Framework whose fifth pillar states bluntly that "if AI can't explain itself, it doesn't belong in hiring." Its AI feature set — Talent Matching, resume anonymisation, scorecard and email generation, interview "Key takeaways" and offer forecasting — is delivered as decision-support that, per its own documentation, "never automatically advances or rejects candidates."
Regulatory exposure
As an ATS deployed by employers hiring in New York City, Illinois, Colorado, and the EU, Greenhouse's AI features (chiefly Talent Matching) fall within the scope of NYC Local Law 144, the EU AI Act's high-risk Annex III employment use case, and Colorado SB 205. Greenhouse has met this exposure unusually well: it holds ISO/IEC 42001 certification (Schellman, February 2026), publishes monthly independent bias audits by Warden AI mapped to LL 144, EU AI Act, Colorado SB 205, and California FEHA, and documents that it does not use customer personal data to train models and assigns no composite ranking score. The principal residual gap is the absence of an EU AI Act Article 27 FRIA template — a near-universal deployer gap rather than a Greenhouse-specific one.
Path to a higher score
Greenhouse already sits near the top of the field. Incremental gains would come from publishing an explicit Article 27 FRIA template or deployer-obligation checklist; surfacing a downloadable model/system card or technical pack alongside its principles page; continuing the consecutive public Warden audit cadence to cross the multi-year bar; and consolidating its scattered support-article governance docs into a single deployer/compliance hub.
Conflicts of interest
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