SHL

SHL is a global talent-assessment provider offering AI-scored cognitive ability, personality, behavioral, situational-judgement, coding, and video-interview assessments that score, rank, and recommend candidates for hiring and development decisions.
AssessmentVideo interviewScreening

HireAIScore rates SHL 51 out of 100 (grade F, Substantial gaps) under rubric v1.0, last reviewed June 7, 2026. SHL ranks 20 of 82 vendors rated in Assessment AI, against a category average of 46. That score is drawn from 19 evidence items across 7 rubric criteria for SHL — 15 with a cited source, 4 recording that nothing was located.

Score breakdown

SHL, criterion by criterion

Each rubric criterion is scored out of 100 on the cited evidence, then weighted into the total.

Weighted contribution shown to the right of each bar.

  1. Article 11 Technical Documentation

    Weight 20% · avg 50

    60

    +12.0

  2. Bias Audit Transparency

    Weight 18% · avg 41

    50

    +9.0

  3. FRIA Support

    Weight 15% · avg 31

    30

    +4.5

  4. Data Governance Disclosure

    Weight 15% · avg 53

    58

    +8.7

  5. Human Oversight Design

    Weight 12% · avg 55

    58

    +7.0

  6. Post-Market Monitoring

    Weight 12% · avg 38

    40

    +4.8

  7. Customer Documentation

    Weight 8% · avg 58

    60

    +4.8

Total weighted score

50.8(rounded to 51 for display)

Category avg is the mean raw score on that criterion across the 82 Assessment AI vendors in scope of this rubric, this one included.

Strongest criterion

Customer Documentation

60raw / 100

Contributes 4.8 to the total. 60 against a 58 category average.

Weakest criterion

FRIA Support

30raw / 100

Contributes 4.5 to the total. 30 against a 31 category average.

Cited evidence

The evidence behind the score

Every score is backed by at least one cited piece of evidence. Open a criterion to see what was found, when, and where.

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Evidence ledger

Items
19
Documentation
15
Absence
4
With a source URL
15 of 19
Source hostnames
1
Fewest items
2
FRIA Support

These figures measure how thoroughly SHL was reviewed, not how SHL performed — an absence row, recording that nothing was located, is counted like any other item.

Article 11 Technical Documentation

3 items60

Bias Audit Transparency

3 items50
  • DocumentationCaptured Jun 7, 2026

    SHL's NYC AEDT Law FAQ states bias audits are performed by an independent auditor and hosted in an internal SHL repository that customers must request via their account manager, at $2,000 USD per instrument - i.e., referenced but not publicly published.

    https://www.shl.com/assets/documents/shl-nyc-aedt-law-july-2023-faq.pdf

  • DocumentationCaptured Jun 7, 2026

    SHL's US Regulatory Compliance page lists 11 AI-scored assessments in scope for NYC LL 144 and describes EEOC/Uniform-Guidelines validation, but contains no publicly downloadable bias-audit report with impact ratios.

    https://www.shl.com/legal/shl-us-regulatory-compliance/

  • AbsenceCaptured Jun 7, 2026

    No independent third-party AI assurance or algorithmic-fairness audit (e.g., Warden AI, Holistic AI, BABL AI, ORCAA) for SHL was found via web search or on SHL's own pages.

    Have a URL that disputes this?

FRIA Support

2 items30
  • AbsenceCaptured Jun 7, 2026

    Searches of shl.com and the web found no SHL EU AI Act Article 27 Fundamental Rights Impact Assessment template or deployer FRIA guidance; SHL's compliance content addresses NYC AEDT, EEOC, and GDPR rather than EU AI Act deployer obligations.

    Have a URL that disputes this?

  • DocumentationCaptured Jun 7, 2026

    SHL states it aims to 'stay ahead of evolving laws like the EU AI Act' and that its AI is 'auditable, transparent, and compliant,' but provides no FRIA template or concrete deployer guidance.

    https://www.shl.com/why-shl/

Data Governance Disclosure

3 items58

Human Oversight Design

2 items58

Post-Market Monitoring

3 items40

Customer Documentation

3 items60

Editorial notes

Company overview

SHL (legal entity SHL Group Ltd.), founded in 1977 and headquartered in the UK with major US operations, is one of the world's largest psychometric and talent-assessment firms, serving most of the FTSE 100 and over half the Fortune Global 500. Its portfolio spans cognitive ability, personality, behavioral, and situational-judgement tests plus AI-scored products acquired largely through its 2019 acquisition of Aspiring Minds, including Smart Interview (video interviewing), Automata (coding), SVAR (spoken language), WriteX (writing), and conversational-chat simulations. SHL grounds its products in industrial/organizational psychology and publishes extensive validity and fairness research, positioning itself as an enterprise-grade, science-led assessment vendor.

Regulatory exposure

SHL's AI-scored assessments score, rank, and recommend candidates, placing them squarely in scope as high-risk AI under EU AI Act Annex III and as AEDTs under NYC Local Law 144. SHL has substantial public-facing material for NYC LL 144 (a detailed FAQ and a US regulatory-compliance page) and grounds validation in the EEOC's AI technical-assistance guidance and the Uniform Guidelines, but its NYC bias audits are conducted by an independent auditor on a per-customer, fee-based, NDA-gated basis (hosted in an internal repository) rather than published openly. SHL maintains a public AI Policy and best-practice whitepaper addressing explainability, fairness, and human oversight, and holds ISO 27001/27018/27701 plus CSA STAR for security/privacy, but publishes no ISO 42001, no EU AI Act-specific deployer guidance, no Article 27 FRIA template, and no independent third-party algorithmic-fairness audit (e.g., Warden AI, Holistic AI, BABL AI).

Path to a higher score

SHL's strongest lever is to convert its already-detailed internal governance into public, downloadable artifacts: publish at least one independent NYC LL 144 bias-audit report (with impact ratios and auditor named) and commit to consecutive yearly public audits; commission an independent third-party AI assurance/audit and publish the result. It should add explicit EU AI Act content, an Article 27 FRIA template or deployer-obligation guidance for high-risk recruitment use, pursue ISO 42001, and publish per-product model cards/instructions-for-use plus a public security disclosure channel, status page, and model-update changelog to evidence post-market monitoring.

Regulatory frame

What applies to assessment AI.

Assessment is the most-regulated surface in AI hiring. NYC LL 144, EEOC and OFCCP frameworks, Illinois HB 3773, and EU AI Act Annex III §4 all apply, with overlap on bias-audit obligations.

Keep reading

All assessment vendorsHow this score is builtAnnex IV section-by-section: what AI hiring vendors must documentWhat makes a defensible NYC LL 144 bias audit

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Ranking

Others rated in Assessment AI

Ranked 20 of 82 by weighted total under rubric v1.0. The ordering is arithmetic on the rubric and carries no view on which tool suits a given hiring process.

  1. #17HiringBranch52F
  2. #18Glider AI52F
  3. #19BrightHire51F
  4. #20SHLThis profile51F
  5. #21Xobin50F
  6. #22Test Partnership50F
All assessment vendors

Conflicts of interest

No vendor pays for placement, scoring, or removal. Casework - the consulting firm that operates this directory - provides paid services to some vendors. Any active or recent (within 24 months) commercial relationship is disclosed on the affected vendor profile and the review is reassigned to an independent reviewer. See the full policy on About.

Casework has no commercial relationship with this vendor.