In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

The black-box nature of current artificial intelligence (AI) has caused some to question
whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has …

Prior work has identified a resilient phenomenon that threatens the performance of human-
AI decision-making teams: overreliance, when people agree with an AI, even when it is …

The proposed EU regulation for Artificial Intelligence (AI), the AI Act, has sparked some
debate about the role of explainable AI (XAI) in high-risk AI systems. Some argue that black …

Practitioners increasingly use machine learning (ML) models, yet models have become
more complex and harder to understand. To understand complex models, researchers have …

The rise of powerful large language models (LLMs) brings about tremendous opportunities
for innovation but also looming risks for individuals and society at large. We have reached a …

Interpretable machine learning has exploded as an area of interest over the last decade,
sparked by the rise of increasingly large datasets and deep neural networks …

Abstract Explainable Artificial Intelligence (XAI) has experienced a significant growth over
the last few years. This is due to the widespread application of machine learning, particularly …

While several types of post hoc explanation methods have been proposed in recent
literature, there is very little work on systematically benchmarking these methods. Here, we …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …