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 …

Most current approaches for protecting privacy in machine learning (ML) assume that
models exist in a vacuum, when in reality, ML models are part of larger systems that include …

Machine learning models are susceptible to a variety of attacks that can erode trust in their
deployment. These threats include attacks against the privacy of training data and …

Introducing noise in the training of machine learning systems is a powerful way to protect
individual privacy via differential privacy guarantees, but comes at a cost to utility. This work …

Machine Learning (ML) models are ubiquitous in real world applications and are a constant
focus of research. At the same time, the community has started to realize the importance of …

Differentially private (DP) optimization is the standard paradigm to learn large neural
networks that are accurate and privacy-preserving. The computational cost for DP deep …

The deployment of deep learning applications has to address the growing privacy concerns
when using private and sensitive data for training. A conventional deep learning model is …

In this paper, we address the problem of privacy-preserving distributed learning and the
evaluation of machine-learning models by analyzing it in the widespread MapReduce …

Automated decision systems are increasingly used to make consequential decisions in
people's lives. Due to the sensitivity of the manipulated data and the resulting decisions …

End users and regulators require private and fair artificial intelligence models, but previous
work suggests these objectives may be at odds. We use the CivilComments to evaluate the …