While artificial intelligence is widely employed in many applications, it is vulnerable to bias
and unethical use. Therefore, fairness evaluation tools and bias mitigation algorithms have …

Automated decision systems are increasingly used to make consequential decisions in
people's lives. Due to the sensitivity of the manipulated data and the resulting decisions …

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …

OBJECTIVE: Ensuring that machine learning (ML) algorithms are safe and effective within all
patient groups, and do not disadvantage particular patients, is essential to clinical decision …

In several real-world applications (eg, online advertising, item recommendations, etc.) it may
not be possible to release and share the real dataset due to privacy concerns. As a result …

In the last decade it became increasingly apparent the inability of technical metrics such as
accuracy, sustainability, and non-regressiveness to well characterize the behavior of …

Decision support systems became ubiquitous in every aspect of human lives. Their reliance
on increasingly complex and opaque machine learning models raises transparency and …

The increasing use of algorithms in allocating resources and services in both private
industry and public administration has sparked discussions about their consequences for …

Machine learning (ML) models cannot neglect risks to security, privacy, and fairness.
Several defenses have been proposed to mitigate such risks. When a defense is effective in …

This paper studies long-term fair machine learning which aims to mitigate group disparity
over the long term in sequential decision-making systems. To define long-term fairness, we …