In classification models, fairness can be ensured by solving a constrained optimization
problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and …

We initiate the study of fair classifiers that are robust to perturbations in the training
distribution. Despite recent progress, the literature on fairness has largely ignored the …

We consider the problem of whether a Neural Network (NN) model satisfies global individual
fairness. Individual Fairness (defined in (Dwork et al. 2012)) suggests that similar individuals …

As machine learning becomes prevalent in a widening array of sensitive applications such
as job hiring and criminal justice, one critical aspect that machine learning classifiers should …

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 …

Algorithmic decision making is employed in an increasing number of real-world applications
to aid human decision making. While it has shown considerable promise in terms of …

Unwanted bias is a major concern in machine learning, raising in particular significant
ethical issues when machine learning models are deployed within high-stakes decision …

Algorithmic Fairness is an established area of machine learning, willing to reduce the
influence of hidden bias in the data. Yet, despite its wide range of applications, very few …

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 past decade has witnessed a rapid growth of research on fairness in machine learning.
In contrast, fairness has been formally studied for almost a century in microeconomics in the …