Gender, Race and Power – AI Now Institute
Over the past year, the AI Now Institute has been examining many of these political and historical intersections through our multi-year research program focused on gender, race, and power in AI. We will shortly publish a report and an academic paper with the first phase of research findings. In light of recent events, we wanted to provide a preview of some of our work as a contribution to the emerging movement and the discussion around it.
some of the things we’ve been reading of late:
– Sara Ahmed, On Being Included: Racism and Diversity in Institutional Life.
– Barriers to Equality in Academia: Women in Computer Science at MIT.
– Joy Buolamwini, Amazon’s Symptoms of FML — Failed Machine Learning — Echo the Gender Pay Gap and Policing Concerns.
– Catherine D’Ignazio and Lauren Klein, Data Feminism.
– Mar Hicks, How to Kill Your Tech Industry.
– Os Keyes, The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition.
– Susan Leavy, Gender Bias in Artificial Intelligence: The Need for Diversity and Gender Theory in Machine Learning.
– Shaka McGlotten, Black Data, in No Tea No Shade: New Writings in Black Queer Studies.
– Vidisha Mishra and Madhulika Srikumar, Predatory Data: Gender Bias in Artificial Intelligence, in Digital Debates — CyFy Journal 2017.
– Joy Rankin, Tech-Bro Culture Was Written in the Code.
– The Invisible Worker #1: The Platform Worker.
(cf. l’article pour les liens)