Group-fairness in classification aims for equality of a predictive utility across different
sensitive sub-populations, eg, race or gender. Equality or near-equality constraints in group …

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

Applying standard machine learning approaches for classification can produce unequal
results across different demographic groups. When then used in real-world settings, these …

We propose a novel algorithm for learning fair representations that can simultaneously
mitigate two notions of disparity among different demographic subgroups in the classification …

We consider a recently introduced framework in which fairness is measured by worst-case
outcomes across groups, rather than by the more standard differences between group …

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 …

The fairness in machine learning is getting increasing attention, as its applications in
different fields continue to expand and diversify. To mitigate the discriminated model …

Algorithmic fairness plays an important role in machine learning and imposing fairness
constraints during learning is a common approach. However, many datasets are imbalanced …

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

In this work, we consider the problem of intersectional group fairness in the classification
setting, where the objective is to learn discrimination-free models in the presence of several …