Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience

Understand

Methods of eXplainable Artificial Intelligence (XAI) are used in geoscientific applications to gain insights into the decision-making strategy of Neural Networks (NNs) highlighting which features in the input contribute the most to a NN prediction.

• Here, we discuss our lesson learned that the task of attributing a prediction to the input does not have a single solution.
• Instead, the attribution results and their interpretation depend greatly on the considered baseline (sometimes referred to as reference point) that the XAI method utilizes; a fact that has been overlooked so far in the literature.
• This baseline can be chosen by the user or it is set by construction in the method s algorithm, often without the user being aware of that choice.