Existing and planned legislation stipulates various obligations to provide information about
machine learning algorithms and their functioning, often interpreted as obligations to …

Machine learning models in safety-critical settings like healthcare are often “blackboxes”:
they contain a large number of parameters which are not transparent to users. Post-hoc …

Post-hoc explanations of machine learning models are crucial for people to understand and
act on algorithmic predictions. An intriguing class of explanations is through counterfactuals …

As machine learning black boxes are increasingly being deployed in domains such as
healthcare and criminal justice, there is growing emphasis on building tools and techniques …

Modern deep learning methods have long been considered black boxes due to the lack of
insights into their decision-making process. However, recent advances in explainable …

The quest to explain the output of artificial intelligence systems has clearly moved from a
mere technical to a highly legally and politically relevant endeavor. In this paper, we provide …

As post hoc explanation methods are increasingly being leveraged to explain complex
models in high-stakes settings, it becomes critical to ensure that the quality of the resulting …

For AI systems to garner widespread public acceptance, we must develop methods capable
of explaining the decisions of black-box models such as neural networks. In this work, we …

The explainable AI literature contains multiple notions of what an explanation is and what
desiderata explanations should satisfy. One implicit source of disagreement is how far the …

Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …