Given the increasing importance of machine learning (ML) in our lives, several algorithmic
fairness techniques have been proposed to mitigate biases in the outcomes of the ML …

Across machine learning (ML) sub-disciplines, researchers make explicit mathematical
assumptions in order to facilitate proof-writing. We note that, specifically in the area of …

As the decisions made or influenced by machine learning models increasingly impact our
lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determining …

Machine learning (ML) is increasingly being used to make decisions in our society. ML
models, however, can be unfair to certain demographic groups (eg, African Americans or …

There is growing awareness that AI and machine learning systems can in some cases learn
to behave in unfair and discriminatory ways with harmful consequences. However, despite …

Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …

Despite a surge of recent advances in promoting machine Learning (ML) fairness, the
existing mainstream approaches mostly require training or finetuning the entire weights of …

In a world of daily emerging scientific inquisition and discovery, the prolific launch of
machine learning across industries comes to little surprise for those familiar with the …

Algorithms are now routinely used to make consequential decisions that affect human lives.
Examples include college admissions, medical interventions or law enforcement. While …

One of the difficulties of artificial intelligence is to ensure that model decisions are fair and
free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and …