Prototypical part neural networks (ProtoPartNNs), namely ProtoPNet and its derivatives, are
an intrinsically interpretable approach to machine learning. Their prototype learning scheme …

Explainable artificial intelligence (XAI) has gained significant attention, especially in AI-
powered autonomous and adaptive systems (AASs). However, a discernible disconnect …

The explainability of black-box machine learning algorithms, commonly known as
Explainable Artificial Intelligence (XAI), has become crucial for financial and other regulated …

Surging interest in deep learning from high-stakes domains has precipitated concern over
the inscrutable nature of black box neural networks. Explainable AI (XAI) research has led to …

Abstract Explainable Artificial Intelligence (XAI) has become increasingly significant for
improving the interpretability and trustworthiness of machine learning models. While …

Explainability techniques are rapidly being developed to improve human-AI decision-
making across various cooperative work settings. Consequently, previous research has …

The increasing involvement of Artificial Intelligence (AI) in moral decision situations raises
the possibility of users attributing blame to AI-based systems for negative outcomes. In two …

Explainable AI (XAI) aims to support appropriate human-AI reliance by increasing the
interpretability of complex model decisions. Despite the proliferation of proposed methods …

As a result of the many recent advancements in artificial intelligence (AI), a significant
interest in the technology has developed from high-stakes decision-makers in industries …

Visualization and machine learning research are both driven by a desire to extract insights
from data. However, the means to this end differ substantially between the two fields. While …