Conformal Prediction for Counterfactual Detection in Concept Learning from Synthetic Visual Patterns

Reliable detection of previously unseen classes under distributional shift remains a central challenge in concept learning and explainable artificial intelligence. In particular, high-performance deep learning models often lack statistically grounded mechanisms to signal when an instance deviates from learned concepts. This paper addresses this limitation by investigating whether conformal prediction can be effectively combined with a YOLOv5 deep learning classifier to enable principled counterfactual detection without prior exposure to the counterfactual class. As a controlled testbed, we employ Kandinsky patterns, a structured benchmark widely used in explainable AI research due to its rule-based generative transparency and suitability for concept learning studies. The proposed framework first classifies valid and invalid patterns and subsequently applies inductive conformal prediction to obtain calibrated prediction sets at a user-defined significance level. Counterfactual instances are, at start, identified based solely on information from known true and false patterns, without explicit training examples of the counterfactual class. Experimental results demonstrate that the conformalized detector reliably identifies a substantial proportion of previously unseen counterfactual patterns while maintaining statistical validity. In addition, the method flags unlabeled (“empty”) instances, thereby providing a principled signal for the emergence of new concepts. By conformalizing YOLOv5 outputs, the approach establishes a statistically sound mechanism for uncertainty-aware detection of divergent classes, contributing to robust and explainable concept learning in structured visual pattern recognition.