Recent rapid progress in machine learning (ML), particularly so‐called 'deep learning', has
led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving …

We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …

At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread
adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the …

We propose a fast, model agnostic method for finding interpretable counterfactual
explanations of classifier predictions by using class prototypes. We show that class …

In this paper we propose a novel method that provides contrastive explanations justifying the
classification of an input by a black box classifier such as a deep neural network. Given an …

The lack of explainability of a decision from an Artificial Intelligence (AI) based" black box"
system/model, despite its superiority in many real-world applications, is a key stumbling …

Several researchers have argued that a machine learning system's interpretability should be
defined in relation to a specific agent or task: we should not ask if the system is interpretable …

Recently, a method [7] was proposed to generate contrastive explanations for differentiable
models such as deep neural networks, where one has complete access to the model. In this …

Artificial Intelligence (AI) has become an integral part of domains such as security, finance,
healthcare, medicine, and criminal justice. Explaining the decisions of AI systems in human …

With the widespread use of Artificial Intelligence (AI), understanding the behavior of
intelligent agents and robots is crucial to guarantee successful human-agent collaboration …