From scientific theory to duality of predictive artificial intelligence models

In studies employing explainable artificial intelligence ({XAI}), model explanation, interpretation, and causality are often not clearly distinguished, leading to potential misunderstandings of model performance or relevance. For predictive {AI} models used in the natural sciences, the path leading from model explanation and interpretation to causal reasoning is of particular importance because it bridges theory and hypothesis-driven experimental design. Selected concepts from scientific theory can be taken into consideration to generate a conceptual framework for putting predictions into scientific perspective and recognizing potential caveats. For explainable models, it is argued that the scientific rationale underlying model derivation plays a decisive role in assessing and understanding predictions and exploring causal relationships, giving rise to the notion of model duality, as introduced herein.