Research & Publications
Don't Predict the Future, Create It: Designing Cooperative Resilience in the AI Transition
As artificial intelligence accelerates the automation of both physical and knowledge work, cooperatives face a distinct challenge: how do you prepare a democratically governed organization for technological disruption without sacrificing the worker sovereignty that makes it worth preserving? In collaboration with ASETT and Mondragon University, we conducted research examining how Mondragon's cooperative network can maintain resilience through the AI transition. Rather than speculating outward about what AI will become, we propose using the future as a mirror — looking inward at how cooperative institutions want to change, or not, across a range of possible economic scenarios. Drawing on Mondragon's history of adapting through past waves of automation, we identified how its governance structures are likely to respond to near-future AI use cases, and where those structures may need reinforcement. Through workshops with cooperative members using speculative design scenarios — from AI-assisted governance to fully automated production — we found that the central challenge is not the technology itself, but the speed at which it arrives, and whether cooperative institutions can act proactively before that speed erodes the democratic foundations underneath them.
Designing for Proactive Accountability: Lessons on Governing Technology from Detroit’s Food Sovereignty Movement
Technology is often built in ways that leave the communities most affected by it with little say over how it works or who benefits. In this work, we engaged with Detroit's food sovereignty movement to study how communities historically marginalized by technology think about accountability and governance. Through a series of speculative design workshops, participants drew on their own experiences with community-owned cooperatives — what they described as "ancestral technologies" — as models for redistributing power. A consistent theme emerged: new technologies would predictably harm their communities because profit would be prioritized over justice, and the usual response — reacting after the damage is done — was not enough. From these insights, we developed the Designing for Proactive Accountability (D4PA) framework, which reconceives governance not as a last-minute ethical check, but as a foundational material of the design process itself. D4PA offers concrete principles for HCI researchers and designers who want accountability to be community-led, formally enforced, and continuously maintained from the very start.
Taxonomizing and Measuring Represenational Harms: A Look at Image Tagging
In this work, we examine computational approaches for measuring the "fairness" of image tagging systems, finding that they cluster into five distinct groups, each with their own analytic foundations. We further identify a diverse range of normative concerns that are often collapsed under the terms "unfairness," "bias," or even "discrimination" when discussing problematic cases of image tagging. In particular, we offer a four-part taxonomy of the harms that may be caused by image tagging systems, offering concrete examples of each. We then consider how different computational measurement approaches map to each of these harms. In so doing, we demonstrate that a wide range of approaches can help measure a wide range of harms. Our findings also highlight that no single measurement approach will provide a definitive result, nor will it be possible to infer from the chosen approach which harm researchers are actually seeking to measure. Lastly, equipped with a more granular understanding of the harms that may be cause by image tagging systems, we show that attempts to mitigate some of these harms may be in tension with one another.
Shape the Vote
Our goal is to provide alternative tools to discuss election forecasting whereby voters are instead reminded and empowered to play their part in democracy.
Detect suspicious IP addresses with the Amazon SageMaker IP Insights algorithm
IP Insights is an unsupervised learning algorithm for detecting anomalous behavior and usage patterns of IP addresses. IP Insights is natively integrated into the Amazon SageMaker platform. Therefore, all you need is to put your data in S3 and you can spin up fully distributed training cluster to train an IP Insights model and then deploy with one-click to an EC2 instance for real-time inference.
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
Researched the application of deep neural networks to survival analysis and demonstrated a deep neural network’s ability to predict the risk of an event occurring (i.e. death of a patient). Demonstrated state-of-the-art performance in predicting a patient’s risk of death and providing them with a personalized treatment recommendation. Released an open-source Python package with a Docker framework to increase the reproducibility of experiments.
#transneeds
Ran a social-media listening campaign to gather data on trans health issues. Analyzed over 12,000 responses and presented findings as policy recommendations to the U.S. Federal Government.