NAI Journal 2026 Journal Article
Leveraging Neurosymbolic AI for Slice Discovery
- Michele Collevati
- Thomas Eiter
- Nelson Higuera
While remarkable recent developments in deep neural networks have significantly contributed to advancing the state-of-the-art in computer vision (CV), several studies have also shown their limitations and defects. In particular, CV models often make systematic errors on important subsets of data called slices, which are groups of data sharing a set of attributes. A slice discovery method (SDM) is meant to detect semantically meaningful slices on which the model performs poorly, called rare slices. We propose a modular neurosymbolic SDM whose distinctive advantage is the extraction via inductive logic programming of human-readable logical rules describing rare slices, and thus enhancing the explainability of CV models. To this end, a methodology for inducing the occurrence of rare slices in a model is presented. We validate the SDM approach on both the synthetic Super-CLEVR and real-world ImageNet datasets. Our experiments demonstrate the complete pipeline: first, we successfully induce targeted rare slices using our taxonomy-based heuristic; second, our neurosymbolic SDM correctly identifies these slices and produces precise, human-readable logical rules to describe them; and finally, these rules are used to guide a data augmentation process that successfully mends model behaviour and improves its predictive performance. 1