Data-driven technologies represent a technological rationality that renders our experiences in the world as distant, unfamiliar, and perhaps even unreliable when compared to the knowledge about the world that our technological systems present us with. Read on Taylor & Francis Online

This essay conceptualizes study the imbrication as a maxim in researching the role of datasets in producing, distributing, and consuming knowledge. Read on Institute of Network Cultures

This paper describes the coupling of machine learning and the social production of risk in general, with specific illustrations drawn from machine learning applications in response to the COVID-19 pandemic. Read on Patterns

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

Sensing Hate, Capturing Dissensus Read on Cultural Anthropology

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

The Shifting Terms of Labor and Compensation in the Platform Economy Read on Social Media + Society

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

There is no one "truth" to data. Read on Harvard Data Science Review