There has been an increasing interest in enhancing the fairness of machine learning (ML).
Despite the growing number of fairness-improving methods, we lack a systematic …

The machine learning security community has developed myriad defenses for evasion
attacks over the past decade. An understudied question in that community is: for whom do …

Adversarial training (AT) is widely considered the state-of-the-art technique for improving the
robustness of deep neural networks (DNNs) against adversarial examples (AE) …

Over the past decade, the machine learning security community has developed a myriad of
defenses for evasion attacks. An understudied question in that community is: for whom do …

Machine learning models that sense human speech, body placement, and other key
features are commonplace in human-robot interaction. However, the deployment of such …

Abstract Recent advances in Machine Learning (ML) and Deep Learning (DL) have resulted
in the wide-spread adoption of models across various application pipelines. However …

Fairness in software systems aims to provide algorithms that operate in a nondiscriminatory
manner, with respect to protected attributes such as gender, race, or age. Ensuring fairness …