The development of AI systems offers great opportunities, but also involves considerable risks. To ensure that an AI system works reliably, trustworthily and in compliance with the law, a systematic approach to quality assurance is essential. This process begins before the actual training of a model and continues into operation. The following section presents a step-by-step approach.
1. Requirements analysis
Before an AI project can begin, the requirements must first be clearly defined. What tasks should the AI perform? Should images be classified, texts summarised or fraud detected? What quality objectives are important, such as accuracy, response speed or fairness requirements? Risks such as discrimination, false alarms or security gaps must also be taken into account from the outset. In addition, regulatory requirements such as the EU AI Regulation or the GDPR must be incorporated. The aim of this phase is to ensure that all parties involved have a common understanding of the purpose, success criteria and limitations of the system.
2. Data quality management
The quality of the data is the basis for the quality of the AI. It is therefore crucial to critically examine the data sources: Are they up to date, complete and truly representative for later use? Incorrect values, outliers or duplicate entries must be cleaned up so as not to mislead the model. The quality of the labels also plays an important role. Only correctly annotated data ensures that the model learns reliably. It is also important to uncover possible biases, for example, if certain groups are underrepresented in the data. Finally, clear documentation of the data origin is essential. The goal is to provide the model with clean, meaningful and balanced data that creates a robust basis for later application.
3. Model development
In the development phase, quality should be integrated directly into the architecture and the approach (‘quality by design’). A key measure is the strict separation of training and test data to avoid overfitting. In addition, suitable metrics must be defined that reflect the specific purpose of use. For example, different standards apply to a medical diagnosis system than to a recommendation system in the e-commerce sector. The model is also tested for robustness by confronting it with distorted or slightly altered inputs. In addition, fairness analyses are used to identify discrimination at an early stage. Finally, explainability methods are integrated to make it transparent why a model has arrived at a particular decision. This results in models that are not only powerful, but also robust, fair and comprehensible.
4. Testing and validation
Before a model goes into productive use, it must be comprehensively tested. This involves both technical functionality, i.e. whether interfaces and data flows work without errors, and checking whether the defined quality indicators are actually achieved. Scenario tests simulate realistic operating conditions, including extreme situations that are rare in practice but particularly critical. To avoid bias, validation should be carried out by an independent team that was not involved in the development. This ensures that the AI works reliably even under realistic conditions.
5. Monitoring during operation
Quality assurance is by no means complete with the go-live; rather, a continuous process now begins. Ongoing monitoring of key performance indicators such as accuracy or response time is just as necessary as the detection of data drift. If the input data changes over time, a model that was originally powerful can quickly become unreliable. Structured error and incident management ensures that anomalies are investigated and rectified. Feedback loops also play an important role: feedback from users helps to improve the model in a targeted manner and adapt it to new requirements. This ensures that the AI remains stable and powerful during operation.
6. Documentation
Complete documentation is ultimately the basis for traceability and compliance. It includes the original requirements and quality objectives as well as a detailed description of the data sources and their processing. The model itself is also documented, including its architecture, training parameters and versions. Test results, test plans and anomalies are just as much a part of the documentation as operating logs with monitoring results, changes and incidents. This information is essential for ensuring transparency towards internal teams, auditors and authorities and for complying with regulatory requirements.
Conclusion: AI quality assurance is a process
Quality assurance in AI systems is not a one-off step, but a continuous process. It begins with requirements analysis, continues through data management and development, and extends to testing, operational monitoring and documentation. Those who systematically establish this process reduce risks, meet regulatory requirements and lay the foundation for reliable, fair and scalable AI solutions. This makes quality assurance an important success factor for companies that want to use AI technologies responsibly.