We investigate fairness in classification, where automated decisions are made for
individuals from different protected groups. In high-consequence scenarios, decision errors …

Mitigating the disparate impact of statistical machine learning methods is crucial for ensuring
fairness. While extensive research aims to reduce disparity, the effect of using a\emph {finite …

Prior work on fairness in machine learning has focused on settings where all the information
needed about each individual is readily available. However, in many applications, further …

We present GetFair, a novel framework for tuning fairness of classification models. The fair
classification problem deals with training models for a given classification task where data …

The use of algorithmic decision making systems in domains which impact the financial,
social, and political well-being of people has created a demand for these decision making …

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

To mitigate the bias exhibited by machine learning models, fairness criteria can be
integrated into the training process to ensure fair treatment across all demographics, but it …

Fairness in machine learning has become a global concern due to the predominance of ML
in automated decision-making systems. In comparison to group fairness, individual fairness …

The goal of fairness in classification is to learn a classifier that does not discriminate against
groups of individuals based on sensitive attributes, such as race and gender. One approach …

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 …