Methodology · monitoring · evidence
How Guardian names production signals, thresholds, and review records
Published statistical methods and documented thresholds—mapped to what you actually run in production so outputs trace to metrics, owners, and follow-up, not to vague “AI governance” language.
This is the reasoning layer behind the operating record in Guardian; legal compliance remains a separate human determination.
What this methodology is
A documented framework for which production signals are measured, how thresholds are set, and what review and evidence records should exist when those thresholds fire—not a substitute for legal judgement or a single “compliance score” as a verdict.
Compliance, risk, legal, and AI teams get shared language: metric → threshold → owner → retained artefact, anchored on one in-scope system first so the pattern can extend without losing traceability.
Product overviewEU AI ActHiring & HR AICredit, fraud & underwriting
Why open methodology matters
For high-risk AI systems, monitoring outputs need to be explainable. If a signal, alert, or score cannot be traced to a documented method, it is difficult to defend in front of a regulator, auditor, internal governance committee, or legal review.
Guardian's approach keeps that logic visible—what is measured, why it matters, which threshold fired, and what follow-up record is expected—so outputs stay useful in operations and defensible in review.
What Guardian measures
These rows are illustrative signal families configured per deployment—not a universal legal mapping of your obligations. Your counsel sets legal context; the table shows how we typically relate inputs to EU AI Act themes for review and evidence.
| Metric | What it shows | Regulatory link |
|---|---|---|
| Demographic parity | Fairness across cohorts | Article 10 / Article 14 |
| Equalised odds | Error-rate equity across groups | Article 10 |
| Model drift | Performance change over time | Article 72 |
| Data quality | Input distribution and anomaly signals | Article 10 |
| Human oversight actions | Review and intervention records | Article 14 |
| Incident frequency | Rate and nature of flagged events | Article 62 |
| Documentation completeness | Coverage of required technical records | Article 11 |
How signals connect to operational review
Guardian maps each monitoring signal to the operational and regulatory context it supports.
When a threshold is crossed, the output should not sit in isolation. It should help teams understand what changed, why it matters, who should review it, and what record should be maintained next.
This does not replace legal interpretation. It connects measurement to action in a governed record. A typical path is the 4-week Readiness Sprint, then day-to-day use in Guardian with EU AI Act context as needed.
What becomes easier with a documented methodology
The same clarity lands in Guardian as timestamps, owners, and retained artefacts—not only in monitoring charts.
- Explaining why a signal or alert was generated
- Showing which metric, threshold, and reference support an output
- Making monitoring outputs easier for compliance, legal, and risk teams to review
- Building a monitoring and evidence baseline that can be expanded over time
Academic and regulatory grounding
Guardian's methodology is developed with academic oversight from Dr. OJ Akintande of DTU Compute, bringing statistical rigor to fairness, drift, and model-risk monitoring.
Metrics and threshold logic are grounded in published statistical methods and relevant regulatory frameworks, including the EU AI Act, NIST AI RMF, ISO 42001, and peer-reviewed fairness research.
The goal is not to make legal determinations automatically. It is to make monitoring outputs more explicit, reviewable, and defensible.
Team
Nordic AI Integrity
Thomas Noba
Co-founder & CEO
Nordic AI Integrity ApS.
Joris Cappa
Co-founder & COO
Nordic AI Integrity ApS.
Dr. OJ Akintande
Technical Advisor
DTU Compute (Technical University of Denmark). ML fairness and model risk specialist.
Frequently asked questions
- Is Guardian’s compliance score a legal determination?
- No. Guardian does not treat a score as a legal verdict. A score is only one monitoring signal among others, used to help teams prioritise review and maintain a defensible evidence record.
- Why publish the methodology openly?
- Because monitoring outputs are more useful when teams can understand and explain them. Open methodology makes it easier to trace outputs back to documented metrics, thresholds, and references.
- How are thresholds set?
- Thresholds are based on documented statistical methods and calibrated to the monitoring context. The goal is to make review triggers explicit rather than opaque.
- Does methodology replace legal review?
- No. The methodology supports monitoring and evidence maintenance. Legal interpretation and compliance determinations still require human review in context.
Put the methodology to work on one system
Pilot discussion to align signal scope; security policy for how we handle data in production.