Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is
an established area in ML. As ML technologies expand their application domains, including …

Deep learning's unprecedented success raises several ethical concerns ranging from
biased predictions to data privacy. Researchers tackle these issues by introducing fairness …

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 …

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases
in historical data used to train them. While computational techniques are emerging to …

Fairness-aware machine learning (fair-ml) techniques are algorithmic interventions
designed to ensure that individuals who are affected by the predictions of a machine …

Ethical bias in machine learning models has become a matter of concern in the software
engineering community. Most of the prior software engineering works concentrated on …

As machine learning algorithms are deployed on sensitive data in critical decision making
processes, it is becoming increasingly important that they are also private and fair. In this …

To enable an ethical and legal use of machine learning algorithms, they must both be fair
and protect the privacy of those whose data are being used. However, implementing privacy …

We introduce a novel technique to achieve non-discrimination in machine learning without
sacrificing convexity and probabilistic interpretation. We also propose a new notion of …

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 …