As black-box machine learning models become more complex and are applied in high-
stakes settings, the need for providing explanations for their predictions becomes crucial …

The decision-making process of many state-of-the-art machine learning models is inherently
inscrutable to the extent that it is impossible for a human to interpret the model directly: they …

As black box explanations are increasingly being employed to establish model credibility in
high stakes settings, it is important to ensure that these explanations are accurate and …

The field of'explainable'artificial intelligence (XAI) has produced highly cited methods that
seek to make the decisions of complex machine learning (ML) methods' understandable'to …

The rise of deep learning in today's applications entailed an increasing need in explaining
the model's decisions beyond prediction performances in order to foster trust and …

Most state-of-the-art machine learning algorithms induce black-box models, preventing their
application in many sensitive domains. Hence, many methodologies for explaining machine …

Methods for interpreting machine learning black-box models increase the outcomes'
transparency and in turn generates insight into the reliability and fairness of the algorithms …

The benefit of locality is one of the major premises of LIME, one of the most prominent
methods to explain black-box machine learning models. This emphasis relies on the …

Artificial Intelligence (AI) has come to prominence as one of the major components of our
society, with applications in most aspects of our lives. In this field, complex and highly …

In the current landscape of explanation methodologies, most predominant approaches, such
as SHAP and LIME, employ removal-based techniques to evaluate the impact of individual …