A feature-based model explanation denotes how much each input feature contributes to a
model's output for a given data point. As the number of proposed explanation functions …

Deep neural network-based methods have recently enjoyed great popularity due to their
effectiveness in solving difficult tasks. Requiring minimal human effort, they have turned into …

Path attribution methods are a popular tool to interpret a visual model's prediction on an
input. They integrate model gradients for the input features over a path defined between the …

Recent work has found that adversarially-robust deep networks used for image classification
are more interpretable: their feature attributions tend to be sharper, and are more …

Interpretability provides a means for humans to verify aspects of machine learning (ML)
models and empower human+ ML teaming in situations where the task cannot be fully …

This tutorial examines the synergistic relationship between explainability methods for
machine learning and a significant problem related to model quality: robustness against …

Abstract Background and Objective: Evaluating the interpretability of Deep Learning models
is crucial for building trust and gaining insights into their decision-making processes. In this …

Deep learning methods for ophthalmic diagnosis have shown success for tasks like
segmentation and classification but their implementation in the clinical setting is limited by …

The evaluation of explainable artificial intelligence is challenging, because automated and
human-centred metrics of explanation quality may diverge. To clarify their relationship, we …

Machine-learning (ML) classifiers are increasingly used to distinguish malware from benign
binaries. Recent work has shown that ML-based detectors can be evaded by adversarial …