We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

Academic and public debates are increasingly concerned with the question whether and
how algorithmic decision-making (ADM) may reinforce social inequality. Most previous …

While machine learning can myopically reinforce social inequalities, it may also be used to
dynamically seek equitable outcomes. In this paper, we formalize long-term fairness as an …

Although many fairness criteria have been proposed for decision making, their long-term
impact on the well-being of a population remains unclear. In this work, we study the …

Deep neural networks (DNNs) are increasingly used in real-world applications (eg facial
recognition). This has resulted in concerns about the fairness of decisions made by these …

Prediction-based decision-making systems are becoming increasingly prevalent in various
domains. Previous studies have demonstrated that such systems are vulnerable to runaway …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …

Decision-making systems increasingly orchestrate our world: how to intervene on the
algorithmic components to build fair and equitable systems is therefore a question of utmost …

We initiate the study of the effects of non-transparency in decision rules on individuals' ability
to improve in strategic learning settings. Inspired by real-life settings, such as loan approvals …