In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …

Abstract Machine Learning has become a popular tool in a variety of applications in criminal
justice, including sentencing and policing. Media has brought attention to the possibility of …

A distinction has been drawn in fair machine learning research between'group'and'
individual'fairness measures. Many technical research papers assume that both are …

Much of the debate on the impact of algorithms is concerned with fairness, defined as the
absence of discrimination for individuals with the same" merit." Drawing on the theory of …

With the surge in literature focusing on the assessment and mitigation of unfair outcomes in
algorithms, several open source 'fairness toolkits' recently emerged to make such methods …

Fairness for Machine Learning has received considerable attention, recently. Various
mathematical formulations of fairness have been proposed, and it has been shown that it is …

Efforts to promote equitable public policy with algorithms appear to be fundamentally
constrained by the “impossibility of fairness”(an incompatibility between mathematical …

In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …

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 …

Ranking items or people is a fundamental operation at the basis of several processes and
services, not all of them happening online. Ranking is required for different tasks, including …