The social awareness around the possibility of machine learning algorithms making biased
decisions has led to an increase in Responsible AI studies in recent years. Algorithmic …

Fairness is a growing concern in machine learning as state-of-the-art models may amplify
social prejudice by making biased predictions against specific demographics such as race …

Due to hard accessibility, real-world adoption of fair representation learning algorithms lacks
the prior knowledge of the sensitive attributes that we wish to be fair with. To address the …

Machine learning models are extensively being used to make decisions that have a
significant impact on human life. These models are trained over historical data that may …

Abstract Representation learning has recently been used to remove sensitive information
from data and improve the fairness of machine learning algorithms in social applications …

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

Training machine learning models with the only accuracy as a final goal may promote
prejudices and discriminatory behaviors embedded in the data. One solution is to learn …

Developing learning methods which do not discriminate subgroups in the population is the
central goal of algorithmic fairness. One way to reach this goal is by modifying the data …

To reduce human error and prejudice, many high-stakes decisions have been turned over to
machine algorithms. However, recent research suggests that this does not remove …

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 …