A trade-off between accuracy and fairness is almost taken as a given in the existing literature
on fairness in machine learning. Yet, it is not preordained that accuracy should decrease …

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 …

Partial Information Decomposition (PID) is a body of work within information theory that
allows one to quantify the information that several random variables provide about another …

In domains ranging from computer vision to natural language processing, machine learning
models have been shown to exhibit stark disparities, often performing worse for members of …

The use of machine learning (ML) in high-stakes societal decisions has encouraged the
consideration of fairness throughout the ML lifecycle. Although data integration is one of the …

Public and private institutions must often allocate scarce resources under uncertainty.
Banks, for example, extend credit to loan applicants based in part on their estimated …

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 …

Feature selection is a crucial step in building machine learning models. This process is often
achieved with accuracy as an objective, and can be cumbersome and computationally …

This chapter investigates the multifaceted approaches nations adopt in governing and
regulating artificial intelligence (AI). It examines the divergent paths taken by the European …

With the development of artificial intelligence, automated decision-making systems are
increasingly integrated into various applications, such as hiring, loans, education …