Mercer | Mettl
Mercer | Mettl is an online talent assessment and remote-proctoring platform offering psychometric, aptitude, coding, and skills tests plus AI-assisted exam proctoring and scoring for hiring, certification, and L&D.
§ 01 — Score breakdown
§ Score breakdown
Category scoring
Weighted contribution shown to the right of each bar.
- 01
Article 11 Technical Documentation
Weight 20%38
+7.6
- 02
Bias Audit Transparency
Weight 18%42
+7.6
- 03
FRIA Support
Weight 15%28
+4.2
- 04
Data Governance Disclosure
Weight 15%55
+8.3
- 05
Human Oversight Design
Weight 12%45
+5.4
- 06
Post-Market Monitoring
Weight 12%38
+4.6
- 07
Customer Documentation
Weight 8%52
+4.2
§ 02 — Strongest · weakest
Strongest category
Data Governance Disclosure
Raw score 55 · contributes 8.3 to total.
Weakest category
Customer Documentation
Raw score 52 · contributes 4.2 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
Mercer | Mettl is an online assessment and remote-proctoring platform founded in 2010 by Ketan Kapoor and Tonmoy Shingal in Gurgaon, India, and acquired by Mercer (part of Marsh McLennan) in October 2018. It provides a large library of psychometric, aptitude, coding, and behavioral assessments, structured and video interviews, and an AI-assisted proctoring suite (SecureProctor, Mettl Secure Browser) that uses webcam, audio, and screen monitoring to flag suspicious activity. The platform serves roughly 2,000 enterprise clients across 80+ countries for hiring, certification, and learning and development.
Regulatory exposure
As an assessment and AI-proctoring vendor scoring and screening candidates, Mercer | Mettl supplies tools that are likely high-risk AEDTs under NYC Local Law 144 and high-risk AI under the EU AI Act, and are in scope for Illinois and Colorado AI-hiring rules. The company publishes a solid information-security and GDPR posture (ISO 27001:2013, ISO 9001:2015, SOC 2 Type 2) and self-reported psychometric fairness claims (adverse-impact testing by age/gender/ethnicity, SIOP/EEOC/APA alignment), but no independent, downloadable NYC LL 144 bias audit, no ISO 42001 AI management certification, no FRIA or Art. 27 deployer guidance, and only general parent-company EU AI Act thought leadership rather than product-specific compliance documentation. Deployers therefore carry most of the public-disclosure and audit burden.
Path to a higher score
Mercer | Mettl could materially raise its score by commissioning and publicly posting an independent, downloadable NYC LL 144-style bias audit (auditor, date, intersectional impact ratios) and renewing it annually; publishing a model/system card or technical pack with an explainability statement for its proctoring and scoring AI; pursuing ISO 42001; releasing explicit EU AI Act deployer guidance and a FRIA template; documenting in-product human-oversight, override, and audit-log controls; and standing up a public status page and security/vulnerability-disclosure channel.
Conflicts of interest
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