Join author Hannah Zeavin for this conversation with Data & Society's Health and Data researcher Livia Garofalo and psychiatrist Dr. Romy Gad el Rab, inspired by Zeavin's new book "The Distance Cure: A History of Teletherapy" (MIT Press). Provocations and Q&A follow the discussion.
Race and technology are closely intertwined, continuously influencing and reshaping one another. While algorithmic bias has received increased attention in recent years, it is only one of the many ways that technology and race intersect in computer science, public health, digital media, gaming, surveillance, and other domains. To build inclusive technologies that empower us all, […]
Prof. Narayanan is Associate Professor of Computer Science at Princeton University, at Princeton University, and Prof. Pasquale is Professor of Law at Brooklyn Law School. The (Public Interest Technology Chat) PITCh is a monthly series of remote conversations between established and early career scholars to support the work of practitioners and academics interested in public […]
Bringing together contributors of the book, this speaker series explores how feminists employ a variety of online platforms, practices, and tools to create spaces of solidarity and to articulate a critical politics that refuses popular forms of individual, consumerist, white feminism in favor of collective, tangible action. This session approaches to exploring networking feminisms. Meet […]
We're excited to announce our next event for the Eyebeam Fold community! If you haven't yet joined the Fold, we invite you to do so now and take part in our virtual event on January 27, 2022, at 12 pm EST featuring artists Kevin Gotkin and Ezra Benus. In this roundtable discussion titled "Accessibility Biases […]
Join author Catherine Knight Steele for this conversation with Data & Society's Policy Research Analyst Serena Oduro, inspired by Catherine Knight Steele's new book "Digital Black Feminism" (NYU Press). Provocations and Q&A follow the discussion. Buy your copy of the book here: https://bookshop.org/a/14284/9781479808380.
Problems of algorithmic bias are often framed in terms of lack of representative data or formal fairness optimization constraints to be applied to automated decision-making systems. However, these discussions sidestep deeper issues with data used in AI, including problematic categorizations and the extractive logics of crowdwork and data mining. In this talk, I make two […]