Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

We review state-of-the-art formal methods applied to the emerging field of the verification of
machine learning systems. Formal methods can provide rigorous correctness guarantees on …

Formal approaches can provide strict correctness guarantees for the development of both
hardware and software systems. In this work, we examine state-of-the-art formal methods for …

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

Fair representation learning provides an effective way of enforcing fairness constraints
without compromising utility for downstream users. A desirable family of such fairness …

Fairness of machine learning (ML) software has become a major concern in the recent past.
Although recent research on testing and improving fairness have demonstrated impact on …

Convolutional neural networks (CNNs) have achieved immense popularity in areas like
computer vision, image processing, speech proccessing, and many others. Unfortunately …

Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the
state of the art in numerous application domains. However, DNN-based decision rules are …

Fair representation learning is an attractive approach that promises fairness of downstream
predictors by encoding sensitive data. Unfortunately, recent work has shown that strong …

Safety is a critical concern for the next generation of autonomy that is likely to rely heavily on
deep neural networks for perception and control. This paper proposes a method to repair …