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

Madeleine Clare Elish uses a case study of "sepsis-watch" to understand how the integration of AI tools is changing the medical space. Read on SSRN

In this paper, authors identify the challenges to integrating fairness into machine learning based systems and suggest next steps. "In this paper, however, we contend that these concepts render technical interventions ineffect... Read on SSRN

Data & Society Fellow Cynthia Conti-Cook and co-authors assess the bias involved in risk assessment tools. "In the top layer, we identify challenges to fairness within the risk-assessment models themselves. We explain type... Read on Criminal Justice and Behavior

How are AI technologies being integrated in health care? What are the broader implications of this integration? Data & Society Researcher Madeleine Clare Elish investigates. "This paper examines the development of a machin... Read on SSRN

In this article, Data & Society Founder and President danah boyd and Researcher Madeleine Clare Elish break down the "magic" narrative around AI systems. "'Big Data' and 'artificial intelligence' have captured the public i... Read on SSRN

This paper is a response to calls for explainable machines by Data & Society Postdoctoral Scholar Andrew Selbst and Affiliate Solon Barocas. "We argue that calls for explainable machines have failed to recognize the connec... Read on Fordham Law Review