Specifically, we summarize our contributions into the following points.

Data attribution methods aim to quantify the influence of individual training samples on the prediction of artificial intelligence (AI) models.

Building on this, our study investigates the interplay between sharpness and diversity within deep ensembles, illustrating their crucial role in robust generalization to both in-distribution (ID) and out-of-distribution (OOD) data.

We propose a probabilistic perspective on adversarial examples, allowing us to embed subjective understanding of semantics as a distribution into the process of generating adversarial examples, in a principled manner.