Explainable AI & Monitoring
AI Explainability & Monitoring
AI decisions must be interpretable – for users, auditors, and regulators. We implement XAI methods and continuous monitoring for your AI systems.
Why Explainability?
AI systems that make decisions must be able to explain them – to users, auditors, and regulators. The EU AI Act prescribes extensive transparency and explanation requirements for high-risk AI under Article 13. Explainable AI (XAI) is not merely a compliance obligation; it is also fundamental to building justified trust in AI systems deployed in consequential contexts.
XAI Methods
We implement established XAI methods including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Attention Visualization. The right method depends on your model type, use case, and compliance requirements – we evaluate the options and implement the most suitable approach.
Continuous Monitoring
AI models change in production – through data drift (shifting input distributions) and concept drift (changing target relationships). We build monitoring pipelines that detect drift early and trigger alerts before model quality degrades, enabling timely retraining and maintaining performance guarantees.
Explainability as a Regulatory Requirement
The EU AI Act (Art. 13) requires high-risk AI systems to provide transparency so that deployers can understand how the system operates and so that competent authorities can assess it. DORA requires AI used in ICT risk management by financial entities to be auditable. Under GDPR Art. 22, individuals have a right to explanation for fully automated decisions. Explainability is not only a technical quality – it is a measurable legal obligation in a growing range of contexts.
Typical Use Cases
Bank with AI-driven credit decisions
A credit institution uses AI for automated credit scoring. Under the EU AI Act and GDPR Art. 22, decisions must be explainable – both to applicants and to supervisory authorities such as BaFin. We implement SHAP-based explanations suitable for regulatory inspections and customer rejection communications.
Insurer with degraded model quality after extreme weather event
An insurance company notices that its AI claims model produces worse results following an extreme weather event. Root cause: data drift caused by changed damage patterns. We establish drift monitoring, identify the trigger, and support retraining on updated data.
Pharmaceutical company facing regulatory authority requirements
A pharmaceutical company must document and explain AI-assisted quality assurance decisions to the EMA and FDA. We develop XAI reports tailored to the specific requirements of regulatory authorities and ensure revision-safe archiving.
Our Services
- SHAP / LIME Implementation
- Model Interpretability Reports
- Data Drift Monitoring
- Concept Drift Detection
- Fairness Monitoring
- Performance Monitoring Dashboards
- Model Audit & Review
- XAI for Regulatory Authorities
Make Your AI Interpretable
We help you make AI decisions transparent, auditable, and compliant.
Request ConsultationFrequently Asked Questions about AI Explainability and Monitoring
What is the difference between explainable AI and transparent AI?
Transparent AI reveals its internal structure (e.g. decision trees). Explainable AI (XAI) makes decisions understandable to humans – even when the model is internally opaque (a black box). XAI methods like SHAP enable explanations for complex models without access to their internal logic.
Which XAI method is right for my model?
This depends on the model type and use case. SHAP works model-agnostically and delivers consistent explanations. LIME is faster but less stable. Attention Visualization is suited to Transformer-based models. We evaluate your specific use case and recommend the appropriate method.
How can I detect data drift early?
Statistical tests such as the Kolmogorov-Smirnov test or Population Stability Index (PSI) measure changes in input data distributions. Monitoring pipelines compute these metrics continuously and trigger alerts when defined thresholds are exceeded – before model quality noticeably deteriorates.
Is AI monitoring possible for third-party models (APIs)?
Yes. Even with API-based AI services, inputs and outputs can be logged, analysed for drift, and monitored for unwanted patterns. Monitoring operates at the interface point – access to the internal model is not required.
Kontakt aufnehmen
Interpretable AI for Your Organisation
Implement XAI and monitoring that builds trust and meets regulatory requirements.