PhD student at MIT, studying how data stewardship and analysis can impact community governance.

Right now, I'm focused on how algorithmic management is changing the reality of work, how data stewardship and participatory design can help create alternate working futures, and on the data rights of platform workers.

Attention Economy #1

May 30, 2020

A test exhibit at Stone & Chalk, an accelerator in South Australia. The online and offline world has been pigeonholed into a constant state of optimization: for attention, resources, and “engagement”. How do these metrics impact the content we create? What should, or shouldn’t we, use them to evaluate?

Attention Economy is a speculative series of continuously-evolving A.I. generated paintings that constantly self-optimize to attract the attention of passerby and viewers. Each installation starts with a random painting, created using a generative deep learning model trained on decades of historic artworks. Using face detection and head-pose analysis to create its own set of “engagement” metrics for each viewer, each piece constantly learns and develops a distinct painting, tailored to the viewers around it.

Note: no data about viewers is saved, collected, or shared in any way. The piece immediately computes anonymous metrics not attached to any identifier, and discards all individual data.