What evidence is needed for EU AI Act conformity assessment?

Article 11 requires technical documentation covering system description, design specifications, development and testing processes, risk management measures, and post-market monitoring plans. Article 12 requires automatic logging of system operations.

How do auditors test for bias in AI systems?

Auditors use disaggregated performance metrics across protected groups, statistical fairness tests, and comparison of outcomes for different demographic segments. The specific methodology depends on the system's context and applicable fairness criteria.

Can AI system logs serve as audit evidence?

Yes, system logs are valuable evidence when they are complete, tamper-resistant, and time-stamped. Auditors should verify that logging is configured to capture relevant events and that logs have not been altered.

How should interview evidence be recorded?

Interview notes should be taken contemporaneously, reviewed with the interviewee for accuracy when possible, and stored with the date, participants, and topics covered. Formal audit interviews are typically not recorded.

What sampling approach works for large AI training datasets?

Statistical sampling methods (random, stratified, systematic) can be applied to training data. The sample size should be justified based on the dataset size, diversity, and the specific risks being assessed.

Quick answer

AI audit evidence includes technical documentation, test results, system logs, interview records, and direct observation of AI system behavior, collected through structured methods that ensure reliability and traceability.

Updated June 2026 · MmowW AI Compliance

Collecting Audit Evidence for AI Systems: Sources, Methods, and Documentation (2026)

Evidence in AI Auditing

Audit evidence is the information collected during an audit to support findings and conclusions. For AI systems, evidence collection presents unique challenges because much of the relevant information is technical, dynamic, and distributed across multiple systems and teams.

Types of AI Audit Evidence

Documentary Evidence

Written records that document the AI system's design, development, and operation.

Technical documentation (model cards, data sheets, architecture diagrams)
Risk assessments and impact assessments
Policies and procedures
Training records and competency certifications
Meeting minutes (governance meetings, management reviews)
Change management records
Incident reports and corrective actions

Test Evidence

Results from testing AI system performance against defined criteria.

Accuracy and performance metrics across different populations
Bias and fairness test results
Robustness testing (adversarial inputs, edge cases)
Security testing results
User acceptance testing outcomes

System Evidence

Data extracted directly from the AI system and its supporting infrastructure.

System configuration records
Audit logs (access, changes, decisions)
Monitoring dashboards and alerts
Model versioning records
Data lineage and provenance records

Testimonial Evidence

Information obtained through interviews with personnel involved in AI system development, operation, and governance.

Evidence Collection Methods

Method	Best For	Considerations
Document review	Policies, procedures, records	Verify currency and approval status
Interviews	Understanding processes, context	Corroborate with documentary evidence
System inspection	Configuration, logs, outputs	Use read-only access to prevent changes
Testing and sampling	Performance, bias, accuracy	Define representative test sets
Observation	Process execution, practices	Record observations contemporaneously
Walkthrough	End-to-end process understanding	Follow a specific transaction or decision

Evidence Quality Criteria

Audit evidence must meet quality criteria to support reliable conclusions.

Relevance: The evidence relates directly to the audit criteria
Reliability: The source and method are trustworthy
Sufficiency: Enough evidence exists to support the finding
Timeliness: The evidence reflects the period under review
Objectivity: The evidence is factual, not based on opinion

AI-Specific Evidence Challenges

Model Opacity

Some AI models are inherently difficult to inspect. For complex models, evidence collection may rely on input-output testing rather than internal inspection. Document the approach used and any limitations.

Data Volume

Training datasets can be extremely large. Auditors typically use sampling strategies rather than reviewing entire datasets. Document the sampling methodology and justify its representativeness.

Dynamic Behavior

AI systems that learn continuously change over time. Evidence collected at one point may not reflect system behavior at another. Use timestamped evidence and capture system state at defined points.

Documentation Requirements

All evidence should be documented with the following information.

Description of what was collected
Source (system, document, interviewee)
Collection date and time
Collection method
Collector identity
Relevance to specific audit criteria
Any limitations or caveats

Evidence Storage and Retention

Store audit evidence securely, with appropriate access controls, for the retention period required by applicable regulations and organizational policies. For EU AI Act conformity assessments, Article 18 requires documentation to be kept for 10 years after the AI system is placed on the market.

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This article is for informational purposes only and does not constitute legal advice. Regulatory requirements change frequently — verify current rules with official sources. Built by Sawai Gyoseishoshi Office, Hiroshima, Japan.