Feminist Data Set – Open Discussions

Back to Rethinking Feminism / Caroline Sinders: Feminist Data Set

The creation of a feminist data set within a historic feminist art space introduces the possibility of data collection as a feminist practice, blurring the lines between art making and activism, with the larger goal of producing a tool to intervene in larger civic and private networks.

As part of Caroline Sinder’s project Feminist Data Set, SOHO20 is hosting a series of workshops as part of the Rethinking Feminism program. Workshops are free and open to the public. We invite all of those interested in the way feminism can impact and is impacted by language to participate.

WORKSHOP EVENTS

March 25, 3-6pm – Caroline Sinders and Natalie Cadranel – open discussion

This discussion will focus on why we must take this opportunity to re-imagine and evolve the archive around mobile media and prioritize InfoSec and threat-modeling as necessary considerations when building archival tools for activist media. We will discuss how to weave inclusivity, privacy, ethical design, egalitarian governance, and human rights into the fabric of these tools.

Natalie Cadranel is the Founder and Director of OpenArchive, a mobile-to-archive preservation ecosystem designed by and for archivists, activists, and citizen journalists. Using participatory research methods and building on contemporary archival theory and practice through the lens of human rights advocacy, she created this open source technology to ethically collect and preserve born-digital media made by at-risk groups. Specifically, to protect media from government and corporate efforts to chill free speech though censorship, privacy breaches, and data loss, while preserving it for legacy access.

April 7 – 3-4:30pm – Caroline Sinders and Hannah Davis

Hannah Davis is a generative musician and independent researcher working in machine learning. Her work falls along the lines of music generation, data sonification, AI, and sentiment analysis. Her algorithm TransProse, which translates novels and other large works of text into emotionally-similar music, has been written up in TIME, Popular Science, Wired, and others.

Hannah is currently working on creating unique datasets for art and machine learning. Through her work on emotions in AI, she’s become particularly interested in the idea of “subjective data” and has started further research into this area. She is a 2017 AI Grant recipient and currently an artist-in-resident at NYU-ITP working on the ML5js library. Her work can be found at www.hannahishere.com and www.musicfromtext.com.

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