Rubric v1.0

Methodology

The full HireAIScore rubric: seven categories, weights, evidence requirements, grade thresholds, update cadence, and conflicts of interest policy.
On this page8 sections
  1. Why we built this rubric
  2. The seven categories
  3. What evidence we look for in each category
  4. How we score
  5. Evidence types
  6. Update cadence
  7. Conflicts of interest
  8. How vendors can request a review or response
  9. Methodology version history

Rubric version v1.0 · effective from 2026-05-20 · last updated 2026-05-21

HireAIScore is an evaluator-driven directory. There are no user reviews and no crowd ratings. Every score in this directory is assigned by the HireAIScore editorial team working against the public rubric below, on the basis of evidence we can show.

This page is the rubric, end to end. It does not move without a methodology version bump, and every change is recorded in the version history at the bottom of this page.

Why we built this rubric

By the end of 2026, anyone deploying an AI hiring tool in the EU, Colorado, Illinois, or New York City is on the hook for a documentation burden that the typical HR team does not have the expertise to satisfy alone. The deployer's obligations - fundamental rights impact assessments, post-market monitoring, human oversight design, bias audits - depend on what the vendor actually provides. A glossy product page is not enough; an Annex IV-aligned technical pack is.

This rubric scores vendors on the things deployers genuinely need from them to meet those obligations. It deliberately weights the categories that the law treats as non-optional over the categories that are nice-to-have.

The seven categories

Each vendor is scored 0–100 in each of the seven categories below. The category weights sum to 1.0; the total score is the weighted sum.

#CategoryWeightWhat we look for
1Article 11 Technical Documentation0.20Does the vendor publish or provide on request Annex IV-aligned technical documentation? Completeness against the 9 sections of Annex IV.
2Bias Audit Transparency0.18Has the vendor published a bias audit per NYC LL 144? How recent? Methodology disclosed? Results published vs gated?
3FRIA Support0.15Does the vendor provide deployers the data needed to complete a Fundamental Rights Impact Assessment under Article 27? Templates, system cards, intended use definitions.
4Data Governance Disclosure0.15Training data sources, validation methodology, performance metrics across demographic subgroups, data retention policies - the surface Illinois HB 3773 reaches.
5Human Oversight Design0.12How does the system support Article 14 human oversight? Override capabilities, confidence thresholds, decision auditability.
6Post-Market Monitoring0.12Article 72 obligations: incident reporting channels, performance drift monitoring, model update disclosure.
7Customer Documentation0.08Instructions for use (Article 13), system limitations clearly stated, intended use boundaries documented - and the input to Colorado's impact assessment.

What evidence we look for in each category

Article 11 Technical Documentation. Public Annex IV-aligned documentation, system cards, technical whitepapers, or a documented availability-on-request process with a named scope and contact path. A page that says "documentation available on request" without specifying what is in the pack scores below a vendor whose pack table-of-contents is public.

Bias Audit Transparency. Published bias audit reports (full or summary), audit methodology, audit cadence statement, third-party auditor identity. NYC LL 144 audits are the baseline; we credit vendors who run audits in jurisdictions that do not require them. Existence is not sufficiency - see what makes an audit defensible and the methodology dispute in Mobley v. Workday.

FRIA Support. FRIA templates, deployer guidance, system cards naming intended use, customer-facing data sheets. The Article 27 obligation falls on the deployer, but a vendor's documentation is what makes a complete FRIA possible. We grade on whether a deployer using only the vendor's public materials could complete each section of the FRIA. That Article 27 duty applies to deployers in the public sector or covered private regulated entities; the 0.15 weight reflects the broad utility of this documentation to every deployer, not a universal legal mandate.

Data Governance Disclosure. Training-data source descriptions, subgroup performance metrics, retention and deletion policy, data-rights process. We treat the absence of subgroup metrics - even when overall accuracy is disclosed - as a substantial gap.

Human Oversight Design. Override and stop controls, confidence threshold configuration, audit log surfaces, escalation paths in product. We credit vendors who design oversight as a first-class workflow, not as a settings toggle.

Post-Market Monitoring. Public incident-reporting channel, model-update changelog, drift monitoring evidence, customer notification protocol. A vendor with no public way to report concerns scores poorly here regardless of what is happening internally.

Customer Documentation. Instructions for use, limitations statement, intended-use boundaries, customer support documentation. Sales decks do not count; only what is given to a deployer post-purchase.

How we score

Each category gets an integer from 0 to 100. The total score is the weighted sum:

total = Σ (category_score × category_weight)

We round the total to the nearest integer for display, but the unrounded total drives the letter grade. There is no curve.

Score rangeGradeLabel
90–100AExemplary
80–89BStrong
70–79CAdequate
60–69DConcerning
0–59FSubstantial gaps

A grade is a statement about a vendor's posture against the rubric, not about the vendor as a company or a product. A C vendor can be the right choice for a given deployer; an A vendor can still be the wrong choice if its product does not fit the role.

Evidence types

Every category score must be backed by at least one piece of evidence. We classify evidence into five types:

  • documentation - a public-facing technical document, whitepaper, or product page produced by the vendor.
  • audit_report - a third-party audit, e.g. a bias audit per NYC LL 144.
  • public_statement - a blog post, press release, regulatory filing, or conference talk.
  • integration - a feature observable in the product. We credit only features we can verify ourselves.
  • absence - a noted lack of evidence after good-faith search. This is itself evidence. When we use absence, we cite the places we looked so the vendor can dispute it with a real URL.

Update cadence

  • Rubric review: annually. A version bump (e.g. v1.0 → v1.1) triggers re-scoring of every vendor before the new version is treated as canonical.
  • Vendor scores: refreshed at least every six months, and immediately on a material vendor change (acquisition, major product launch, published audit, new system card).
  • Last-reviewed dates are visible above the fold on every vendor profile. Scores are also browsable by category and side by side.

Conflicts of interest

HireAIScore is a sister property to Casework, the AI hiring compliance firm. The two properties share operators but make decisions in separate forums - Casework engagement findings do not modify HireAIScore scores, and Casework engagement leads do not score vendors. The full policy, including the "no vendor money in either direction" and "named recommendations disclose relationships" clauses, is on the About page.

The three load-bearing rules for the rubric itself:

  1. No vendor pays for placement, scoring, or removal. Ever. No exceptions.
  2. If Casework has had a paid commercial relationship with a vendor in the past 24 months, that fact is disclosed on the vendor's profile and on the About page.
  3. Conflicts are disqualifying. If a member of the editorial team has a personal or financial relationship with a vendor, they do not score that vendor. Every vendor profile attributes its score to the HireAIScore editorial team, shown in the "Reviewed by" line on the profile.

We treat the conflicts disclosure as a load-bearing piece of the rubric. A vendor profile that should carry a disclosure and does not is a bug; please report it via the address on the About page.

How vendors can request a review or response

Vendors are not consulted before a profile is published. The site is evaluator-driven; consulting vendors before publication would introduce the exact pressure the rubric is designed to resist.

After publication:

  • To request a re-score when the underlying evidence has changed (new bias audit, new technical pack, published system card), email the address on the About page with the new evidence. Re-scores typically land within 30 days.
  • To submit a response - a vendor right-of-reply - email the same address. Responses are published verbatim, attributed to a named respondent, on the vendor's profile, above the conflicts disclosure.

We do not edit vendor responses for tone or accuracy. We do reserve the right to decline responses that contain personal data, threats, or claims about other vendors.

Methodology version history

VersionEffective fromChanges
v1.02026-05-20Initial rubric. Seven categories tied to the EU AI Act, Colorado AI Act, Illinois HB 3773, NYC LL 144, and current case-law.

Job-search tools for candidates are scored on a separate rubric with its own categories - see the job-search directory.

Rubric js-v1.0

Job-seeker tools are scored on a different rubric.

Auto-apply, resume AI, and job-matching tools are not automated employment decision tools - they act for the candidate, not the employer, so the v1.0 compliance criteria above don’t apply. They are graded instead on job-seeker protection: the same 100 points, 7 categories, and the same conflicts and independence rules.

See the scored tools

Rubric js-v1.0

100 points

CategoryWeightWhat it measures
Application Honesty20%Does the tool tailor applications truthfully, or does it fabricate or inflate experience? Guardrails against lying on applications; how it handles gaps and stretch claims.
Data & Credential Safety20%How the tool stores a job seeker's resume and personal data, whether it takes job-board / email / LinkedIn credentials, encryption, SOC 2, and whether data is sold or shared.
User Control & Transparency15%Review-before-submit vs. blind mass-apply, editability of AI output, and whether the tool discloses to the user (and downstream) that AI was used.
Employer & ATS Legitimacy15%Does it submit applications through legitimate channels, or scrape/spam in ways that risk the user's account bans or violate job-board / ATS terms of service? Quality over raw volume.
Effectiveness Evidence12%Are outcome claims (response rates, interviews landed) substantiated with real data, or unbacked marketing ('10x more interviews')?
Pricing & Cancellation Fairness10%Transparent pricing, refund policy, no dark patterns, and an easy cancellation path - job seekers are often financially stretched.
Support & Accountability8%Real, reachable support; company transparency (who runs it, where); and an accountable track record rather than a disposable brand.