According to scientific realists, the success of a scientific theory provides strong evidence that it is (approximately) true (Putnam, 1975). In response, antirealists argue that the theories we have are successful because they are survivors of a selection process where unsuccessful theories are rejected, so truth is not necessary to explain success (van Fraassen 1980). This paper argues that the training and testing process of artificial intelligence is structurally analogous to the selection process of scientific theories. Convolutional Neural Networks (CNN) achieve human-level performance in image classification through iterative training procedures that adjust weights and biases to minimise errors. Moreover, recent techniques in explainable AI (XAI) can approximate concept-level interpretations of the CNN’s structure. Some of these concepts align with human concepts, while others do not, even when predictive performance is comparable. The CNN is interpreted as encoding a structural representation of the data, analogous to how a scientific theory represents phenomena. To the extent that the AI classifier uses similar concepts to humans, we have support for realist interpretations of successful representation. Conversely, divergence from human concepts lends weight to antirealist interpretations.
