Trustworthy AI is a critical issue in machine learning where, in addition to training a model
that is accurate, one must consider both fair and robust training in the presence of data bias …

Fairness and robustness are critical elements of Trustworthy AI that need to be addressed
together. Fairness is about learning an unbiased model while robustness is about learning …

Optimizing prediction accuracy can come at the expense of fairness. Towards minimizing
discrimination against a group, fair machine learning algorithms strive to equalize the …

There is growing awareness that AI and machine learning systems can in some cases learn
to behave in unfair and discriminatory ways with harmful consequences. However, despite …

As the decisions made or influenced by machine learning models increasingly impact our
lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determining …

Motivated by concerns surrounding the fairness effects of sharing and transferring fair
machine learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The …

Motivated by concerns that machine learning algorithms may introduce significant bias in
classification models, developing fair classifiers has become an important problem in …

Both fair machine learning and adversarial learning have been extensively studied.
However, attacking fair machine learning models has received less attention. In this paper …

Much of the previous machine learning (ML) fairness literature assumes that protected
features such as race and sex are present in the dataset, and relies upon them to mitigate …

In this paper, we cast fair machine learning as invariant machine learning. We first formulate
a version of individual fairness that enforces invariance on certain sensitive sets. We then …