Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A
better understanding of the needs of XAI users, as well as human-centered evaluations of …

Attribution methods are a popular class of explainability methods that use heatmaps to
depict the most important areas of an image that drive a model decision. Nevertheless …

A multitude of explainability methods has been described to try to help users better
understand how modern AI systems make decisions. However, most performance metrics …

In recent years, concept-based approaches have emerged as some of the most promising
explainability methods to help us interpret the decisions of Artificial Neural Networks (ANNs) …

In this paper we explore the unique modality of sketch for explainability emphasising the
profound impact of human strokes compared to conventional pixel-oriented studies. Beyond …

Affective computing often relies on audiovisual data to identify affective states from non-
verbal signals, such as facial expressions and vocal cues. Since automatic affect recognition …

Feature visualization has gained significant popularity as an explainability method,
particularly after the influential work by Olah et al. in 2017. Despite its success, its …

Feature visualization has gained substantial popularity, particularly after the influential work
by Olah et al. in 2017, which established it as a crucial tool for explainability. However, its …

Saliency methods are a popular class of feature attribution explanation methods that aim to
capture a model's predictive reasoning by identifying" important" pixels in an input image …

Music signals are difficult to interpret from their low-level features, perhaps even more than
images: eg highlighting part of a spectrogram or an image is often insufficient to convey high …