Interpretable machine learning: Explaining Change (Project C03)

Today, machine learning is commonly used in dynamic environments such as social networks, logistics, transportation, retail, finance, and healthcare, where new data is continuously being generated. In order to respond to possible changes in the underlying processes and to ensure that the models that have been learned continue to function reliably, they must be adapted on a continuous basis. These changes, like the model itself, should be kept transparent by providing clear explanations for users. For this, application-specific needs must be taken into account. The researchers working on Project C03 are considering how and why different types of models change from a theoretical-mathematical perspective. Their goal is to develop algorithms that efficiently and reliably detect changes in models and provide intuitive explanations to users.

Muschalik, M., Fumagalli, F., Hammer, B., Hüllermeier E. (2022). Agnostic Explanation of Model Change based on Feature Importance. Künstl Intell. doi: 10.1007/s13218-022-00766-6