Explainability & Monitoring
Artificial intelligence makes decisions that impact people, processes, and business models. However, the more complex the models, the more difficult it often is to understand exactly how these decisions are made. For many companies—especially in regulated or safety-critical sectors—this is a risk that cannot be ignored.
We help make AI systems understandable and manageable. This begins with the selection of suitable model architectures and analysis methods that provide insights into the decision logic. The goal is not only technical explainability, but also the ability to prepare results in a way that is understandable for specialist departments, auditors, and end users.
However, explainability alone is not enough. An AI model is not a static product; it evolves over time—be it due to changing data, new application contexts, or external influences. Therefore, we rely on continuous monitoring. We develop monitoring strategies that identify deviations, so-called model drifts, early on, thus maintaining control over the system.
In safety-relevant applications, we complement these approaches with human-in-the-loop concepts: Humans maintain supervision, make the final decision, or intervene when critical thresholds are reached. This creates a balance between automation and responsibility.
With a well-thought-out explainability and monitoring concept, companies not only gain technical confidence in their AI – they also create the basis for acceptance, quality assurance, and regulatory traceability.