The Norwegian Centre for Knowledge-driven Machine Learning

About Integreat

Integreat is a Norwegian Centre of Excellence (SFF), funded by the Research Council of Norway. The centre aims is to develop knowledge-driven machine learning, where data are combined with statistics, logic and domain knowledge to make artificial intelligence (AI) more precise, ethical and sustainable.

Machine learning lies at the core oe3sdf artificial intelligence and is widely used in a range of critical systems across sectors. At the same time, modern machine learning raises a number of challenges related to explainability, fairness and the handling of uncertainty. Integreat develops new methodological frameworks designed to address these challenges.

NR contributes primarily thorugh methodological development in explainability and anomaly detection under certainty. Our work is characterised by a strong foundation in statistical modelling combined with modern machine learning techniques.

Explainable artificial intelligence

Many machine learning models function as “black boxes,” making it difficult to understand why a model produces a particular result. Deep learning is highly effective at identifying complex patterns in data, but it often provides limited insight into how decisions are made. By contrast, statistical models are typically more interpretable but may be less flexible when dealing with highly complex data structures.

At NR, we develop methods that combine the strengths of these approaches. We place particular emphasis on modelling the interactions between variables and on developing explanations that are both methodologically robust and practically applicable. The aim is to build models that not only deliver accurate predictions, but can also be justified and verified.

Anomaly detection

Many machine learning models are built on the assumption that data remain relatively stable over time. When the underlying data change, a model’s precision and stability may deteriorate. Ensuring reliable operations therefore requires the ability to detect anomalies quickly and accurately.

We conduct basic research in anomaly detection and develop statistical and machine learning-based methods for detecting anomalies in data streams. Our work focuses on reliable and adaptable algorithms that can handle evolving data patterns while providing clear and well-defined criteria for what constitutes a deviation.

The methods have broad application potential, ranging from monitoring AI-based systems to other domains where detecting changes or unexpected patterns over time is essential. Our aim is to both strengthen the quality and trustworthiness of data-driven systems and to advance theoretical and methodological understanding within anomaly detection.

Explore our focus areas

To learn more about Integreat
and our role in the centre, get in touch.