Algorithmic fairness aims to address the economic, moral, social, and political impact that
digital systems have on populations through solutions that can be applied by service …

Research to date aimed at the fairness, accountability, and transparency of algorithmic
systems has largely focused on topics such as identifying failures of current systems and on …

Research in Fairness, Accountability, Transparency, and Ethics (FATE) 1 has established
many sources and forms of algorithmic harm, in domains as diverse as health care, finance …

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 …

A recent normative turn in computer science has brought concerns about fairness, bias, and
accountability to the core of the field. Yet recent scholarship has warned that much of this …

Algorithmic systems are known to impact marginalized groups severely, and more so, if all
sources of bias are not considered. While work in algorithmic fairness to-date has primarily …

A growing number of oversight boards and regulatory bodies seek to monitor and govern
algorithms that make decisions about people's lives. Prior work has explored how people …

Conventional algorithmic fairness is West-centric, as seen in its subgroups, values, and
methods. In this paper, we de-center algorithmic fairness and analyse AI power in India …

In recent years, fairness has rightly become an important topic of interest in the AI, law,
ethics, and political community. In the computer science (CS) community, people have …

Algorithmic systems increasingly shape information people are exposed to as well as
influence decisions about employment, finances, and other opportunities. In some cases …