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.
HireAIScore rates SHL 51 out of 100 (grade F, Substantial gaps) under rubric v1.0, last reviewed June 7, 2026. SHL ranks 18 of 49 vendors rated in Assessment AI, against a category average of 48. 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.
§ 01 - Company facts
- Legal name
- SHL Group Ltd.
- Founded
- 1977
- Headquarters
- United Kingdom · GB
- Pricing tier
- Enterprise
- Market side
- Employer-side (AEDT)
- Categories
- AssessmentVideo interviewScreening
- Website
- shl.com
These are company details, not findings. They are editable by a verified representative and carry no weight in the score, which is built only from the cited evidence below.
§ 02 - Score breakdown
§ Score breakdown
Category scoring
Weighted contribution shown to the right of each bar.
- 01
Article 11 Technical Documentation
Weight 20%60
+12.0 · category avg 53
- 02
Bias Audit Transparency
Weight 18%50
+9.0 · category avg 47
- 03
FRIA Support
Weight 15%30
+4.5 · category avg 32
- 04
Data Governance Disclosure
Weight 15%58
+8.7 · category avg 55
- 05
Human Oversight Design
Weight 12%58
+7.0 · category avg 55
- 06
Post-Market Monitoring
Weight 12%40
+4.8 · category avg 39
- 07
Customer Documentation
Weight 8%60
+4.8 · category avg 59
Category avg is the mean raw score on that criterion across the 49 Assessment AI vendors in scope of this rubric, this one included.
§ 03 - Strongest · weakest
Strongest category
Customer Documentation
Raw score 60 · contributes 4.8 to total.
60 against a 59 category average
Weakest category
FRIA Support
Raw score 30 · contributes 4.5 to total.
30 against a 32 category average
§ 04 - Cited evidence
Download diligence record→§ Evidence
Cited per category
Every score is backed by at least one cited piece of evidence.
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
- DocumentationCaptured Jun 7, 2026
Public 11-page SHL Artificial Intelligence Policy (v1.0, Dec 2024) sets out an ethical AI framework (UK Data Ethics Framework, 7 principles), governance structure, and a documented review of legacy AI products against explainability, transparency, and accountability criteria.
https://www.shl.com/assets/documents/shl-artificial-intelligence-policy-december-2024.pdf
- DocumentationCaptured Jun 7, 2026
SHL best-practice whitepaper 'The Ethical and Effective Use of AI' includes dedicated sections on AI explainability ('the why and how'), transparency ('no black-box algorithms'), and documenting validity in a technical manual to professional standards.
- AbsenceCaptured Jun 7, 2026
No ISO/IEC 42001 AI-management certification, public per-product model/system cards, or instructions-for-use packs were found across shl.com legal, security-and-compliance, and resource pages.
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.
- 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.
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.
- 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.
Data Governance Disclosure
3 items58
- DocumentationCaptured Jun 7, 2026
AI Policy states SHL does not use visual-appearance/facial features and does not use candidate demographic information (race, age, gender, location) as algorithm inputs, and applies a data-minimization ('proportional to need') principle.
https://www.shl.com/assets/documents/shl-artificial-intelligence-policy-december-2024.pdf
- DocumentationCaptured Jun 7, 2026
SHL's Security & Compliance portal lists ISO 27001, ISO 27018, ISO 27701, ISO 22301, ISO 9001, and Cyber Essentials Plus, with SOC 2 Type II reports available under NDA.
- DocumentationCaptured Jun 7, 2026
SHL participates in the Cloud Security Alliance STAR program (registry entry 'shl-group-ltd'), evidencing cloud security/privacy transparency.
https://www.shl.com/legal/security-and-compliance/cloud-security-alliance-star/
Human Oversight Design
2 items58
- DocumentationCaptured Jun 7, 2026
SHL's whitepaper devotes a section to Human Oversight, describing human-in-the-loop / human-on-the-loop / human-in-command models, recruiter override of AI recommendations, and a best practice that no AI system should make a final high-stakes hiring decision without the possibility of human intervention.
- DocumentationCaptured Jun 7, 2026
SHL's NYC AEDT FAQ frames assessment output as guidance that is not relied on solely, not weighted above other criteria, and does not overrule human decision-making in the recruitment process.
https://www.shl.com/assets/documents/shl-nyc-aedt-law-july-2023-faq.pdf
Post-Market Monitoring
3 items40
- DocumentationCaptured Jun 7, 2026
AI Policy commits to ongoing monitoring of AI systems for accuracy, fairness, and security, periodic compliance/fairness review, and maintaining a reporting mechanism for AI-related incidents or breaches.
https://www.shl.com/assets/documents/shl-artificial-intelligence-policy-december-2024.pdf
- DocumentationCaptured Jun 7, 2026
SHL states its Information Security Program is continually reviewed and validated by independent regulatory institutions and third-party security assessment organizations.
https://www.shl.com/legal/security-and-compliance/information-security/
- AbsenceCaptured Jun 7, 2026
No public system status page, model-update changelog, continuous-monitoring dashboard, or published responsible-disclosure/security-contact channel was found on shl.com.
Customer Documentation
3 items60
- DocumentationCaptured Jun 7, 2026
Dedicated US Regulatory Compliance page explains NYC LL 144 applicability, lists in-scope AI assessments, and describes SHL's bias-audit process for customers.
- DocumentationCaptured Jun 7, 2026
SHL resource hub publishes downloadable whitepapers on AI in talent assessment, transparency, trust, and responsible innovation, including questions buyers should ask AI vendors.
- DocumentationCaptured Jun 7, 2026
Security & Compliance documentation portal centralizes certifications, GDPR materials, and NDA-gated documents (SOC 2, penetration tests, DR, security/GDPR policies) for customers.
§ 05 - 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.
§ Compare
Build any comparison→SHL against its nearest-scoring peers.
In Assessment AI.
§ Others rated in Assessment AI
All assessment vendors→Ranked 18 of 49 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.
- 15Criteria53F
- 16HiringBranch52F
- 17BrightHire51F
- 18SHLThis profile51F
- 19Jobma49F
- 20X0PA AI48F
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.