Feature Extraction: The Aesthetics and Politics of Algorithms
The Feature Extraction ASSEMBLY included a series of talks, demos, workshops, and discussion groups that brought together artists and researchers engaged with machine-learning. Under the banner of “artificial intelligence,” machine learning has become central to interlocking domains of political-economic control, including: predictive policing, financialization, ad-tech, social media, and logistics. Unlike the proprietary algorithms used in these applications, artistic uses of machine learning allow people to experience and engage with algorithms directly. In this series, we paired artistic uses of machine learning with scholarly research to explore the social repercussions of algorithmic governance under algorithmic capitalism.
Organized by Blaine O’Neill and Ulysses Pascal | Blaine.d.oneill[at]gmail.com | upascal[at]gmail.com | http://feature-extraction.ai/
Documentation / ResourcesLink to researchWatch Pitch Presentation
Blaine O’Neill, Ulysses Pascal, Bela Abolfathi, Carla Orendorff, Cecile Thompson-Hannant, Chase Niesner, Chris Tyler, Dean Lenoir, Harry Hvdson, Leah Wulfman, Leming Zhong, Muxi Zhuo, Parag Mital, Riley O’Neill, Sara Drake, Selwa Sweidan, Soffi Stiassni
The Feature Extraction ASSEMBLY group used their microgrant ($225) to pay honorariums for guest speakers.