Participation-washing could be the next dangerous fad in machine learning | MIT Technology Review

This is a much-needed intervention in the field of machine learning, which can be excessively hierarchical and homogenous. But it is no silver bullet: in fact, “participation-washing” could become the field's next dangerous fad. That’s what I, along with my coauthors Emanuel Moss, Olaitan Awomolo, and Laura Forlano, argue in our recent paper “Participation is not a design fix for machine learning.” Ignoring patterns of systemic oppression and privilege leads to unaccountable machine-learning systems that are deeply opaque and unfair. These patterns have permeated the field for the last 30 years. Meanwhile, the world has watched the exponential growth of wealth inequality and fossil-fuel-driven climate change. These problems are rooted in a key dynamic of capitalism: extraction. Participation, too, is often based on the same extractive logic, especially when it comes to machine learning.