Explainable Artificial Intelligence (XAI) is gaining importance in various fields, including forestry and tree-growth modelling. However, challenges such as evaluating model interpretability, lack of transparency in some XAI methods, inconsistent terminology, and bias towards specific data types hinder its integration.
In their article our colleagues Anahid Jalali, Alexander Schindler and Anita Zolles propose combining long short-term memories (LSTMs) with example-based explanations to enhance tree-growth models’ interpretability.The full article can be found here.

Image source: Karlsruhe Institute of Technology.



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