01287nas a2200109   4500008004100000245004400041210004300085260001600128520096900144100001901113856004501132       2023                        eng d00aExplain and Interpret Few-Shot Learning0 aExplain and Interpret FewShot Learning  bCEUR-WS.org3 aRecent advancements in Artificial Intelligence have been fueled by vast datasets, powerful computing resources, and sophisticated algorithms. However, traditional Machine Learning models face limitations in handling scarce data. Few-Shot Learning (FSL) offers a promising solution by training models on a small number of examples per class. This manuscript introduces FXI-FSL, a framework for eXplainability and Interpretability in FSL, which aims to develop post-hoc explainability algorithms and interpretableby-design alternatives. A noteworthy contribution is the SIamese Network EXplainer (SINEX), a post-hoc approach shedding light on Siamese Network behavior. The proposed framework seeks to unveil the rationale behind FSL models, instilling trust in their real-world applications. Moreover, it emerges as a safeguard for developers, facilitating models fine-tuning prior to deployment, and as a guide for end users navigating the decisions of these models1 aFedele, Andrea  uhttps://ceur-ws.org/Vol-3554/paper38.pdf