Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …

Fair machine learning is receiving an increasing attention in machine learning fields.
Researchers in fair learning have developed correlation or association-based measures …

With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

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 …

In recent years fairness in machine learning (ML) has emerged as a highly active area of
research and development. Most define fairness in simple terms, where fairness means …

Over the past several years, multiple different methods to measure the causal fairness of
machine learning models have been proposed. However, despite the growing number of …

In recent years, the problem of addressing fairness in machine learning (ML) and automatic
decision making has attracted a lot of attention in the scientific communities dealing with …

Decision-making systems based on AI and machine learning have been used throughout a
wide range of real-world scenarios, including healthcare, law enforcement, education, and …

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 …

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