Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

The emerging field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to
today's powerful but opaque deep learning models. While local XAI methods explain …

Applying traditional post-hoc attribution methods to segmentation or object detection
predictors offers only limited insights, as the obtained feature attribution maps at input level …

What is it to interpret the outputs of an opaque machine learning model? One approach is to
develop interpretable machine learning techniques. These techniques aim to show how …

Understanding complex machine learning models such as deep neural networks with
explanations is crucial in various applications. Many explanations stem from the model …

How machines could teach physicists new scientific concepts | Nature Reviews Physics Skip to
main content Thank you for visiting nature.com. You are using a browser version with limited …

Humans use abstract concepts for understanding instead of hard features. Recent
interpretability research has focused on human-centered concept explanations of neural …

Concept-based interpretations of black-box models are often more intuitive for humans to
understand. The most widely adopted approach for concept-based interpretation is Concept …

One reason for the positive impact of Pre-trained Language Models (PLMs) in NLP tasks is
their ability to encode semantic types, such as 'European City'or 'Woman'. While previous …

While explainability is becoming increasingly crucial in computer vision and machine
learning, producing explanations that are able to link decisions made by deep neural …