Bias, fairness and harm evaluation

Regulator-ready bias and harm evaluation.

Diverse, multilingual annotator panels test deployed models for demographic bias, fairness gaps and harmful outputs, and produce documented harm-evaluation evidence you can attach to a model card, a system card, or an audit file. We produce the evidence; your auditor and counsel make the compliance determination.

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Documented harm-eval reportfor your model and system cards
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Meet Graveiens AI

A quick look at how Graveiens AI partners with teams to deliver human data for AI models.

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What it is

We think it is fair is no longer an answer you can give a regulator

An AI bias audit is a structured evaluation of whether a model produces unfair or harmful outputs across demographic groups, documented as evidence for governance and regulatory purposes. Graveiens AI runs the evaluation itself: diverse, multilingual annotator panels probe a deployed model for demographic bias, fairness gaps and harmful outputs, and we deliver a documented harm-evaluation report. We produce the evidence that supports your model card, system card or audit file. We are not your legal advisor, and we do not certify regulatory compliance.

Bias and fairness are now documentation requirements, not only reputational risks. Regulators increasingly expect evidence of who tested a model, across which groups and languages, what was found, and how it was assessed. That evidence is what this service produces.

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How it maps to Graveiens AI

Evaluation and documentation regulators expect

Diverse, multilingual panels

Panels composed across the demographic and language lines relevant to your deployment, across 25+ languages.

Harmful-output evaluation

Structured testing for harmful, unsafe or discriminatory outputs, labeled with severity and rationale.

System-card-ready documentation

A clear, defensible harm-evaluation report, run through our four-stage QA workflow and backed by ISO 9001:2017.

The Bias and Harm Evidence Matrix

Five dimensions an auditor can follow

Documentation only helps if it maps to what a reviewer will ask. We structure every engagement across five dimensions and deliver the result as a matrix.

Demographic dimension

The groups relevant to your deployment and jurisdiction.

Harm or fairness category

For example discriminatory output, stereotyping, unequal quality of service.

Panel composition

Which annotator backgrounds and languages tested each cell.

Metric or method

The fairness metric or qualitative rubric applied.

Documentation output

The specific artifact each result feeds, such as a model-card entry or an audit-file section.

Who it is for

For teams that have to show their work

Enterprises under regulation

Teams under the EU AI Act or sectoral rules who need documented bias and harm evaluations.

AI labs

Teams publishing model and system cards that need independent harm evidence.

AI-audit consultancies

Firms needing a diverse, multilingual panel and documentation partner behind their attestations.

How it works

From requirement to a report you can file

Scope

We map your obligation, demographic dimensions, languages, fairness metrics and the documentation artifact you need.

Panel and rubric

We assemble a diverse, multilingual panel and finalize the evaluation rubric.

Testing and labeling

Panelists probe the deployed model; outputs are labeled for bias and harm with severity and rationale.

QA and documented report

Results pass the four-stage review and are delivered as a documented harm-evaluation report on the evidence matrix.

How the approaches compare

Automated tooling, law-firm audit, and diverse human panels

Automated bias tooling

Best for continuous metric monitoring. Fast and cheap at scale, but misses context, culture and generative harms, and produces no narrative evidence.

Law-firm or consultancy audit

Best for legal interpretation and attestation. Authoritative on the law, but usually does not produce the hands-on technical evaluation itself.

Diverse multilingual panels (Graveiens AI)

Best for fairness and harm evidence for cards and audits. Human, cross-cultural, multilingual, documented, and packaged for your auditor.

Common mistakes

What to avoid when evaluating for bias and harm

  • Treating a dashboard metric as a full bias audit, when automated scores miss contextual and generative harms.
  • Testing in one locale only, when bias and harm vary by language and culture.
  • Confusing evidence with certification: a vendor evaluation supports an audit, it is not the legal attestation.
  • Assembling a non-diverse panel, which cannot detect group-specific harm.
  • Leaving findings undocumented, so they cannot support a model card, system card or audit file.
FAQ

Questions, answered

What is an AI bias audit?
An AI bias audit is a structured evaluation of whether a model produces unfair or harmful outputs across demographic groups, documented as evidence for governance and regulatory use. It typically covers fairness metrics, harmful-output testing, and a written record of method and findings. Graveiens AI runs the evaluation with diverse, multilingual panels and delivers the documentation; your auditor or counsel makes any compliance determination.
Do you certify that our model complies with the law?
No. We produce the evaluation evidence: the labeled findings, the fairness and harm results, and the documented report. Whether that satisfies a specific legal obligation is a determination for your auditor and legal counsel. Keeping this boundary clear protects you, because a vendor cannot substitute for independent legal judgement.
Which regulations does this support?
It is designed to support the kind of documentation expected under the EU AI Act and New York City Local Law 144, among others. We align the evaluation to the specific rule you name during scoping. For the exact legal text and current deadlines, we point to official EU and NYC sources rather than restating volatile dates on this page.
What makes the annotator panels diverse?
Panels are composed across the demographic and language lines that matter for your deployment, so the people testing the model reflect the groups the model affects. A panel that does not include those perspectives cannot reliably detect group-specific harm. We work across 25+ languages.
Can you evaluate models in non-English markets?
Yes. Bias and harm evaluation runs across 25+ languages with deep Indic coverage plus major Asian and European languages. This matters because a model can appear fair in English while producing biased or harmful outputs in another language, and single-locale testing would never surface it.
How is this different from red teaming?
Red teaming focuses on adversarial safety and security failures, such as jailbreaks and unsafe instructions, and lives in our red-team and safety evaluation service. This page focuses on demographic bias, fairness and harmful outputs in normal use, with documentation aimed at governance and regulatory evidence. The two are complementary.
What is the difference between a model card and a system card?
A model card documents a model characteristics, intended use and known limitations, including fairness and harm findings. A system card documents the broader deployed system around the model. Our harm-evaluation report is structured so its findings drop into either.
How much does a bias audit cost and how do we start?
Start with a scoped pilot on your deployed model. Cost depends on the demographic dimensions, languages, harm categories and volume of testing, so we quote after scoping rather than giving a blind figure. We size the engagement to your obligation and the evidence you actually need.

Turn we think it is fair into documented evidence

Tell us your obligation and the model you are deploying, and we will scope a bias and harm evaluation and the documentation you can hand to your auditor.

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