AI Quality Testing Framework:
Structure, Application, and Benefits
Artificial intelligence is finding its way into more and more companies — from chatbots and recommendation systems to AI-supported decisions in HR or finance. But for the providers of many AI products, one key question arises:
How can I demonstrate that an AI is powerful, secure, and trustworthy?
This is exactly where the AI Quality Testing Framework comes in. We provide practical help to companies and organizations in evaluating and improving their AI in a transparent manner before problems arise in production, with customers, or in audits.
The framework offers companies a simple and flexible way to test the quality of AI systems and thus gain competitive advantages. The audit can be carried out at different levels of depth. We offer a 3-stage package model (Core / Plus / Deep) that covers requirements from a document-based initial review to reproducible technical validation.
In this article, we explain what the AI Quality Testing Framework is and how the audit process works.
Why an AI Quality Testing Framework?
AI affects not only systems, but also people – users, customers, citizens, or employees. AI makes decisions, prioritizes content, recommends measures, or automates steps that were previously performed by humans. The more influence AI gains, the more important it becomes to ask whether AI is actually doing what it is supposed to do.
The AI Quality Testing Framework offers a structured approach to testing the quality of an AI system and providing binding proof of its quality. The focus is on three aspects:
1. Is the AI good enough?
This involves analyzing the quality, robustness, and performance of an AI system or AI component in real-world use.
2. What are the risks?
This concerns issues such as bias, fairness, data protection, security, or lack of system control.
3. How can quality be proven?
Robust documentation with (technical) evidence of AI system quality is being developed.
The aim is to demonstrate the quality characteristics of an AI and thereby build trust.
What distinguishes the AI Quality Testing Framework?
AI Quality & Testing Hub GmbH is a partner of Mission AI Consortium, initiated by the Federal Ministry of Digital Affairs, which has developed an AI quality standard for low-risk AI systems. We have expanded the Mission AI standard to include additional AI quality frameworks and best practices so that it can be easily and flexibly applied to different AI quality issues in different use cases and industries.
A special feature of the AI Quality Testing Framework is its compatibility with the European AI Regulation (EU AI Act). This came into force in 2024 and affects providers and operators of AI systems to varying degrees. A number of transitional provisions apply to the AI Act, which means that individual requirements will only come into effect gradually. At present, there is a lack of harmonized standards, which means that the practical implementation of legal requirements is only possible to a limited extent. In addition, the EU Commission has proposed extensive amendments to the law. As a result, AI providers and operators face the difficulty of not knowing which compliance requirements the legislator will impose in the foreseeable future.
This is where the compatibility of the AI Quality Testing Framework comes into play: if new requirements of the AI Act become binding in the future, the framework can be adapted or expanded accordingly. Duplication of effort is avoided.
The audit according to the AI Quality Testing Framework in brief
Who is the audit aimed at?
The audit in accordance with the AI Quality Testing Framework is aimed at companies and organizations that integrate artificial intelligence into their products or business processes and want to manage risks, quality requirements, and necessary evidence—internally or externally to customers, authorities, sponsors, investors, or regulators—in a structured and reliable manner.
What does the audit involve?
- Quality testing of AI systems or components according to clearly defined criteria that are understandable for both technical teams and management
- Risk-oriented audit depth, i.e., the scope and level of detail are based on the protection requirements, risk, and potential impact of the AI system
- Standardized testing methodology including checklists, defined evaluation criteria, and structured evidence requirements
- Documented results in the form of a comprehensible testing and evaluation report
- Optional supplementary recommendation, technical validation, or statement/badge for internal or external communication
What is the result of the audit?
- An assessment with a clear classification of which aspects of the AI system are uncritical, critical, or still unclear
- An evidence register that transparently shows which evidence supports which assessment or statement
- A prioritized list of measures with short-term improvements and structural recommendations for action
- Optional: a verifiable statement documenting the performance of the quality check that can be used by internal and external stakeholders.
The audit process in detail
The complete audit according to the AI Quality Testing Framework goes through a 6-step process:
- Definition of the purpose
- Identification of risks
- Determination of quality requirements
- Provision of evidence
- Validation of evidence
- Preparation of the test report
Below is a brief explanation of the individual steps.
1. Definition of the intended purpose
First, the specific use case and purpose of the AI are defined. This includes, for example, the following aspects:
- Context of use, input and output range
- User groups and affected parties
- Architecture description, including components
- Information about operation (cloud/on-premise, continuous learning, etc.)
- Exclusions of certain aspects of use, if applicable
As a result, the AI is clearly specified so that the specific test context is apparent.
2. Identification of risks
The AI Quality Testing Framework is based on the consideration of six fundamental quality dimensions. These are:
- Data quality, data protection, data governance
- Non-discrimination (fairness)
- Transparency (explainability, documentation)
- Human oversight and control
- Reliability (performance, robustness)
- AI-specific cybersecurity
Risks posed by AI are systematically identified along these quality dimensions. This is based on different categories of damage, for example in relation to life and limb, fundamental rights, data protection, or property. The result is an AI-specific risk profile.
3. Definition of quality requirements
The specific risk profile is then used to derive the concrete quality requirements for AI. This is a crucial step in the process of implementing a “tailor-made” validation of quality requirements. Only a precise analysis of which evidence and tests are actually necessary and appropriate makes the AI Quality Testing Framework modular and flexible, so that it can be used in different application scenarios and application domains.
4. Provision of evidence
The necessary evidence for the quality requirements is compiled and provided. The evidence is suitable for verifying the implementation of the specific quality requirements in a comprehensible manner. This can include, for example:
- Documentation (processes, policies, models, data descriptions)
- Technical evidence (test results, logs, code excerpts, metrics)
- Existing certifications (e.g., GDPR, ISO 27001)
The result is a validatable information basis.
5. Validation of evidence
This is followed by the validation of the evidence, i.e., the actual testing of the AI. To this end, we also draw on an extensive collection of testing methods and tools for methodological and technical validation. Technical evidence must be reliable and reproducible. The result ensures that the evidence is correct, plausible, and repeatable.
6. Preparation of the test report
The final test report contains:
- System description and context of use
- Documented evidence (measures, evidence)
- Test depth, validations, responsible persons
- Formal declaration of accuracy
- Recommendations, if applicable
The result is a complete and comprehensible documentation of the AI validation.
Conclusion
Passing an audit based on the AI Quality Testing Framework offers companies a clear strategic advantage.
- It not only serves as reliable proof of quality, but also creates genuine operational and long-term added value. The structured audit process enables the early detection of systemic risks – for example, with regard to data quality, model robustness, or governance – while strengthening trust and reputation among customers, partners, and supervisory authorities.
- Repeated application of the framework professionalizes internal development and documentation processes, creates clear responsibilities, and increases traceability throughout the entire AI lifecycle. In addition, a successful audit facilitates preparation for regulatory requirements, particularly in the context of the EU AI Regulation, as key principles of European regulation are already taken into account and potential gaps can be identified at an early stage.
- As the AI Quality Hub in Hesse – supported by VDE e.V. and the State of Hesse – we combine technical standardization and testing expertise with a public mandate and bring this perspective to the further development of the framework. Companies benefit from increased trustworthiness, reduced risks, optimized development processes, and clear competitive advantages. A documented audit process also creates a quality label that transparently demonstrates compliance with defined standards.
For organizations that already use AI systems or are planning to introduce them, this voluntary quality certification represents a significant advantage – both in terms of internal quality assurance and with regard to future mandatory regulatory requirements.
The audit can be carried out at different levels of depth. We offer a 3-stage package model (Core / Plus / Deep) that covers requirements from a document-based initial review to reproducible technical validation.
Please feel free to contact us!