validant.ai is an independent assurance platform for trustworthy AI, organised around three bodies: the model, the organisation and the person. Today it runs AI fairness and explainability audits and digital-trust audits on the iceberg.digital® framework; self-sovereign identity and signed agent mandates are the next body we are bringing into the platform.

What does validant.ai do for AI fairness?

It runs end-to-end AI fairness and explainability audits: detecting bias and proxy variables, mitigating disparities across data, models, LLMs and agents, explaining decisions with SHAP, LIME and counterfactuals, and monitoring fairness in production with auditor-grade, regulator-ready reports.

Who is validant.ai for?

Anyone who wants AI to stay fair, understandable and under human control. That includes risk, compliance, legal and ML teams proving an AI system is fair, explainable, trustworthy and accountable under the EU AI Act, NIST AI RMF and ISO/IEC 42001, and just as much the everyday person who wants to safeguard their own data and keep control of their agency. We deliberately keep it plain enough for everyone to use, not only specialists.

What is Verifiable Trust Infrastructure (VTI), and how does validant.ai relate to it?

VTI is an open specification, currently a working draft, developed in the Decentralized Trust Graph Working Group run jointly by Trust over IP and the Decentralized Identity Foundation. It defines how people, communities, organisations and the AI agents acting for them vouch for each other, delegate authority and revoke it without a central gatekeeper. validant.ai's founder is a contributor at both foundations and active in that working group, and validant.ai's seals, decision receipts and agent mandates are designed to plug into VTI as it matures.

01INTRO

Trustworthy AI, within everyone's reach

Make AI fair, explainable, provable and accountable: make AI trustworthy.

Expert-grade AI assurance, built for every team.

We test AI systems for discrimination, explain their decisions in plain language, and turn the result into a proof anyone can check. For the governance you need today, and for a future where AI agents act on our behalf.

Check a seal yourself

Our founder contributes to open trust standards at Trust over IP and DIF. Why it matters

Ecosystem

Scroll

02HOW IT WORKS

How it works

Measure. Explain. Prove.

The same three steps for a hiring model, a credit score or an AI agent. Each one ends in evidence someone else can check, not a slide someone has to believe.

01
Measure
We test whether an AI system treats groups of people differently, across 47 fairness metrics, with the statistics to show how sure we are.
You getA plain-language verdict, with the numbers behind it.
02
Explain
We show which factors drive each decision and by how much, so “the model is unfair” becomes “this feature drives it, and by this much”.
You getExplanations a regulator, a manager and the person affected can each read.
03
Prove
We seal the result as a signed credential. Anyone can check it is authentic, unaltered and not revoked, with no login and without trusting us. It covers one system at one moment, not every decision forever.
You getA Trust Seal for the system, a Decision Receipt for the person.

Illustrative data

A model scores 40 applicants from each of two groups with the same spread of qualifications. Move the shortlist line and watch who gets through.

Group A

19 of 40 shortlisted

Group B

7 of 40 shortlisted

Shortlist everyone scoring at least58

Selection-rate ratio

0.37

Group A 48% · Group B 18%. Group B is shortlisted at 0.37× the rate.

What drives the gap

Postcode. It stands in for group membership and costs group B points it did not earn.

A result that fails is not sealed.

The four-fifths rule is one screening test, not the whole fairness question. A real audit runs 28 modules with confidence intervals, not one ratio.

Our demo case, end to end

One rejected job application, carried from the model to the individual.

The same decision, followed across all three bodies, with nothing asserted that a stranger cannot check. It is the shortest honest answer to what we build, which is why we use it as our worked example rather than a diagram.

The model. A CV screening model is measured, and the seal's subject is that AI system at that moment. The engine signs the numbers; our server derives the verdict from them.
The organisation. The deployer is named on the credential as accountable owner, next to the issuing identity and legal entity. It cannot assert its own result.
The person. The applicant gets a Decision Receipt in her own wallet instead of a form rejection: why, the decisive factor, her route to human review, and a link back to the seal she can check herself.

How the seal works Check a seal yourself

03ASSURANCE

Three bodies · one assurance

Every AI decision touches three parties. We assure all three.

The model that decides, the organisation that answers for it, and the person it decides about. Trust holds only when all three can be checked, never one alone.

Why trust is an orbit, not a pillar

Editorial illustration of a long colonnaded courthouse hallway in perspective, with a single figure walking toward the vanishing point

Metaphor

The hall of due process

Fairness in ML is not a feeling, it is a corridor of evidence. Each column is a fairness metric the model must pass through before it reaches a decision. The figure walking toward the vanishing point is the model under audit.

the model

01Live

fair.AI fairness & explainability

What your model actually does, with the receipts to prove it.

Detect, mitigate, explain and monitor bias across the whole pipeline, from training data to LLMs. SHAP, LIME and counterfactuals turn “the model is unfair” into “this feature drives it, and by this much”, with the statistics to back it and EU AI Act mapping built in.

28 assessment modules across the full lifecycle
43 discrimination patterns · US / EU / CH jurisdictions
Explainable AI built in · SHAP, LIME and counterfactuals
Bootstrap, Bayesian and permutation testing

See in the platform

Metaphor

The Swiss Federal Palace

Civic institutions are where digital trust either holds or breaks. We chose Bern because Switzerland is where Validant is built and where digital sovereignty is taken as a serious craft, not a slogan.

the organisation

02Planned

accountable.Digital trust

Proof that your organisation keeps its promises.

Built on the iceberg.digital® framework, we audit whether your digital systems behave as promised: in governance, in engineering, and for the people who use them. Most of that work happens below the waterline, which is where trust is actually won or lost.

Ten trust constructs · 128 measurable cues
Governance, engineering, agency and institutional layers
Explicit and implicit signals, continuously assured

Editorial illustration of a hand holding up a teal ID card against a city skyline, evoking self-sovereign identity

Metaphor

The credential in your hand

Identity should fit between your fingers, not in someone else's database. The hand is yours. The card is the proof. The city behind it is everywhere it can be presented and verified, with no platform in between.

the person

03Planned

yours.Identity & agency

A record of every AI decision, held by the person it is about.

A Decision Receipt gives the person affected their own copy: why the decision fell as it did, and how to ask for a human review. Next come portable credentials and signed agent mandates, so an agent acting for you can prove what it may do, and you can take that back.

Decision Receipt in the person's own wallet
Trust Experience Library of first-person patterns
Credentials and signed agent mandates on the roadmap

04FILM

Brand film

The Algorithm Raised Me.

It promised to show her the world. What it learned, instead, was exactly what would keep her watching, until it became the quiet voice deciding what she wanted. A short film about the systems that raise a generation now, and the line between being served and being shaped.

Choose your edition

Share this film

05STANDARDS

Open standards · VTI

An open trust layer, for people and the AI that acts for them.

Whether a person proves who they are, an organisation answers for its AI, or an agent acts on someone's behalf, the proof is only worth something if anyone can check it. That takes shared infrastructure, not one more platform.

The Verifiable Trust Infrastructure (VTI) is that layer, being specified in the open by Trust over IP and DIF. Our seals, receipts and mandates are designed to plug into it as it matures.

Read the VTI draft specificationWorking draft 0.2 · not yet ratified

01Where trust is needed
People, organisations and their AI
A person proving who they are, a company answering for its model, an agent acting for someone.
02What we build
Trust Seal · Decision Receipt · Agent Mandate
The proofs we build: a result anyone can verify, a receipt the person keeps, authority an agent can show.
03Where our founder contributes
Verifiable Trust Infrastructure
How people, communities, organisations and agents vouch for each other, delegate and revoke, with no gatekeeper.
04Open foundations
W3C DIDs and Verifiable Credentials
The open identifiers and credential formats everything above is built on.

Checkable by anyone

A proof is only useful if a stranger can verify it. Shared infrastructure means no single company decides who is trusted, us included.

People stay in control

A person holds their own credentials, vouches for others, and can hand authority to an agent and take it back, without a platform in between.

Built in the open

VTI is specified in the joint Trust over IP and DIF Decentralized Trust Graph Working Group, where our founder is a contributor.

06PLATFORM

The Validant platform

One platform, three bodies, every claim auditable.

Fairness, digital trust and identity run as modules on one platform. Pick what you need, share the evidence, and keep every claim open to inspection.

What we will not negotiate

Five commitments, built into the runtime.

Standards before features

W3C Verifiable Credentials, DIDs and the open work at Trust over IP and DIF come first, with MLflow and pytest hooks for the tools you already run. We extend ecosystems, we do not lock them in.

Rigour a peer reviewer accepts

Bootstrap and Bayesian intervals, permutation testing, effect sizes, multiple-testing correction. Every claim survives a peer reviewer reading the table.

Compliance is the default

EU AI Act risk classification, GDPR-aligned privacy tiers and 43 historical discrimination patterns across US, EU and Swiss jurisdictions, wired into every assessment.

Auditable in production

Real-time drift detection, a fairness gate before release and four-eyes validation. What an agent does on your behalf is logged, signed and revocable.

Privacy and sovereignty by default

Three privacy tiers per dataset: k-anonymity, differential privacy or exact values. Credentials and mandates are held by the person, never by us. No silent telemetry, no shadow profile.

Open the platform

07LIBRARY

vfairness · open libraryBeta

The fairness library that powers the platform.

vfairness is the engineering core inside validant.ai: a Python library for measuring, mitigating, explaining and monitoring fairness across the whole pipeline, LLMs included. Tap any number below for a plain explanation.

2: AI system types live
43: Discrimination patterns
47: Fairness metrics
3: Intervention stages
40+: Bias-mitigation methods
28: Capability modules

ProductOpen to read

2 May 2026

Inside Trust Signal: the weekly newsletter our AI team writes itself

Every Tuesday, an eight-agent AI team scouts, scores and drafts the week in AI trust and fairness. Here's how it works — and how to get it.

Read

FAQ

Frequently asked questions

What is validant.ai?: validant.ai is an independent assurance platform for trustworthy AI, organised around three bodies: the model, the organisation and the person. Today it runs AI fairness and explainability audits and digital-trust audits on the iceberg.digital® framework; self-sovereign identity and signed agent mandates are the next body we are bringing into the platform.
What does validant.ai do for AI fairness?: It runs end-to-end AI fairness and explainability audits: detecting bias and proxy variables, mitigating disparities across data, models, LLMs and agents, explaining decisions with SHAP, LIME and counterfactuals, and monitoring fairness in production with auditor-grade, regulator-ready reports.
Who is validant.ai for?: Anyone who wants AI to stay fair, understandable and under human control. That includes risk, compliance, legal and ML teams proving an AI system is fair, explainable, trustworthy and accountable under the EU AI Act, NIST AI RMF and ISO/IEC 42001, and just as much the everyday person who wants to safeguard their own data and keep control of their agency. We deliberately keep it plain enough for everyone to use, not only specialists.
What is Verifiable Trust Infrastructure (VTI), and how does validant.ai relate to it?: VTI is an open specification, currently a working draft, developed in the Decentralized Trust Graph Working Group run jointly by Trust over IP and the Decentralized Identity Foundation. It defines how people, communities, organisations and the AI agents acting for them vouch for each other, delegate authority and revoke it without a central gatekeeper. validant.ai's founder is a contributor at both foundations and active in that working group, and validant.ai's seals, decision receipts and agent mandates are designed to plug into VTI as it matures.

Make AI fair, explainable, provable and accountable: make AI trustworthy.Make AIfair.

Measure. Explain. Prove.

Measure

Explain

Prove

Every AI decision touches three parties. We assure all three.

What your model actually does, with the receipts to prove it.

Proof that your organisation keeps its promises.

A record of every AI decision, held by the person it is about.

The Algorithm Raised Me.

An open trust layer, for people and the AI that acts for them.

People, organisations and their AI

Trust Seal · Decision Receipt · Agent Mandate

Verifiable Trust Infrastructure

W3C DIDs and Verifiable Credentials

Checkable by anyone

People stay in control

Built in the open

One platform, three bodies, every claim auditable.

Five commitments, built into the runtime.

Standards before features

Rigour a peer reviewer accepts

Compliance is the default

Auditable in production

Privacy and sovereignty by default

The fairness library that powers the platform.

Signal.

The Preemptive Assurance Paradox: Who Guards the AI Guardrails?

Precision Is Not Proof: The Trap in Every AI Fairness Verdict

The Trust Problem Nobody Wants to Name

When a Model Becomes a Munition

Agents Will Act for Us. Who Vouches for Them?

Digital Trust Is an Orbit, Not a Pillar

Two Views of One Decision: Trustworthy and Explainable AI in Practice at HSLU

The Wrong Question, Asked at Scale

No System Has Ever Been Fair

Two new demo videos: see Pulse run an audit, and the Modules behind every verdict

Bias is the Foundation

Inside Trust Signal: the weekly newsletter our AI team writes itself

Get the week in AI trust and fairness.

Frequently asked questions

Make AI fair, explainable, provable and accountable: make AI trustworthy.