Predictions obtained by, eg, artificial neural networks have a high accuracy but humans
often perceive the models as black boxes. Insights about the decision making are mostly …

AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …

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

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 …

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 …

This paper contributes to the growing literature in empirical evaluation of explainable AI
(XAI) methods by presenting a comparison on the effects of a set of established XAI methods …

Abstract Current developments in Artificial Intelligence (AI) led to a resurgence of
Explainable AI (XAI). New methods are being researched to obtain information from AI …

Black box machine learning models are currently being used for high-stakes decision
making throughout society, causing problems in healthcare, criminal justice and other …

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 …

Recent work on interpretability in machine learning and AI has focused on the building of
simplified models that approximate the true criteria used to make decisions. These models …