Our study of the history of impact assessments shows that "impacts" are an evaluative construct that enable institutions to identify and ameliorate harms experienced because of a policy decision or system. Read on Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Kellie Owens and Alexis Walker list some concrete steps to enable anti-racist practices in medical research and practice. Read on Nature

This paper critically examines existing modes of participation in design practice and machine learning. Read more

Because AIAs are still an incipient governance strategy, approaching them as social constructions that do not require a single or universal approach offers a chance to produce interventions that emerge from careful deliberation. Read on Social Science Research Network

How can human rights serve as a framework for the governance of AI systems? Read on ACM FAT* 2020

A study of AI in clinical care use, with a particular focus on the sepsis detection system, "Sepsis Watch." Read on ACM FAT* 2020

Emanuel Moss translates critical race theory into concepts legible to machine learning practitioners. Read on FAT* Conference 2019

Studying AI agents as if they are animate moves responsibility for the behavior of machines away from their designers, thereby undermining efforts to establish a professional ethics code for AI practitioners. Read on Nature

Manny Moss and Mona Sloane argue for a sociotechnical approach to the design, development, and governance of AI systems. Read on Nature

In this article, Research Lead Madeleine Clare Elish investigates who bears the responsibility when an automated system fails. "Just as the crumple zone in a car is designed to absorb the force of impact in a crash, the human ... Read on Engaging Science, Technology, and Society