Understanding the landscape of potential harms from algorithmic systems enables
practitioners to better anticipate consequences of the systems they build. It also supports the …

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 …

In this article, we focus on the growing evidence of unintended harmful societal effects of
automated algorithmic decision-making in transformative services (eg social welfare …

Abstract The Algorithmic Impact Assessment is a new concept for regulating algorithmic
systems and protecting the public interest. Assembling Accountability: Algorithmic Impact …

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 …

These are legitimate long-term worries. But they are not all we have to worry about, and
placing them center stage distracts from ethical questions that AI is raising here and now …

NeurIPS 2020 requested that research paper submissions include impact statements on"
potential nefarious uses and the consequences of failure." However, as researchers …

Mitigating bias in algorithmic systems is a critical issue drawing attention across
communities within the information and computer sciences. Given the complexity of the …

Industrial, academic, activist, and policy research and advocacy movements formed around
resisting 'machine bias', promoting 'ethical AI', and 'fair ML'have discursive implications for …