Organized by MozFest
MozFest is a unique hybrid: part art, tech and society convening, part maker festival, and the premiere gathering for activists in diverse global movements fighting for a more humane digital world.
Over the last twelve years, MozFest has fueled the movement to ensure the internet benefits humanity, rather than harms it. As the festival matures, we remain focused on our work to build a healthier internet and more Trustworthy AI.
As researchers and practitioners work to develop fairer, more just algorithmic systems, they often encounter the need for more data, especially data that includes sensitive attributes such as race, ethnicity, gender, and sexuality. Without these attributes in collected datasets, it is often unclear how one would go about testing their systems for disparities across groups or possible blindspots in the final system. Collecting these attributes, however, carries its own suite of risks around privacy, self-determination, and mistreatment.
In this MozFest session, PAI’s McKane Andrus, Sonam Jindal, and Sarah Villeneuve explored the question of what types of supporting infrastructure would be required to collect various types of data alongside sensitive attribute data in a fair or responsible way. (We use ‘infrastructure’ here to refer to the diverse set of, often unacknowledged, physical, digital, social, conceptual, and/or legal frameworks that directly enable a system to be produced and operated.) Using tools from speculative design, they looked at a future artifact and worked through what types of infrastructure would be required in this future world for that artifact to exist.
Through this process they began to answer questions such as: What about current data governance practices prevents responsible data collection? What types of changes to data governance and data infrastructures are needed to handle sensitive data? How do the characteristics of the data being collected (e.g. how it is collected, who it is collected from/by, what the intended uses are) change the infrastructural requirements?
Senior Research Associate
Program and Research Lead for AI, Labor and the Economy
Lead of Fairness, Transparency, and Accountability & ABOUT ML