The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

With the rapid growth of information, recommender systems have become integral for
providing personalized suggestions and overcoming information overload. However, their …

A number of exciting advances have been made in automated fact-checking thanks to
increasingly larger datasets and more powerful systems, leading to improvements in the …

The importance of explanations (XP's) of machine learning (ML) model predictions and of
adversarial examples (AE's) cannot be overstated, with both arguably being essential for the …

In recent years, AI explainability (XAI) has received wide attention. Although XAI is expected
to play a positive role in decision-making and advice acceptance, various opposing effects …

The same method that creates adversarial examples (AEs) to fool image-classifiers can be
used to generate counterfactual explanations (CEs) that explain algorithmic decisions. This …

The literature on adversarial attacks in computer vision typically focuses on pixel-level
perturbations. These tend to be very difficult to interpret. Recent work that manipulates the …

Lipschitz constrained networks have gathered considerable attention in the deep learning
community, with usages ranging from Wasserstein distance estimation to the training of …

A significant robustness gap exists between machine intelligence and human perception
despite recent advances in deep learning. Deep learning is not provably secure. A critical …

This paper considers the problem of helping humans exercise scalable oversight over deep
neural networks (DNNs). Adversarial examples can be useful by helping to reveal …