UC Berkeley

Advancements in machine learning hold unprecedented potential to help humans understand and shape our world, from deriving data-driven policies with global prediction systems to combating long-standing prejudice in social decision-making. However, in practice, machine learning is not achieving its potential. Algorithmic systems that are theoretically well motivated fail to live up to anticipated performance in the real world, and all too often, they exacerbate inequality rather than relieve it. In order to translate potential into real benefit, machine learning systems need to address the context of the domain they’re applied in to better interface with system intent and system impact.

In this thesis, we present research on the context-aware design of machine learning systems that interface with individuals, our environment, and our societies. We emphasize the core themes of intent, impact, and context through three interweaving threads: (i) contrasting algorithmic impact with desired intent, (ii) contextualizing learning algorithms through structures in input data, and (iii) advancing machine learning with remotely sensed data as precise applications of intent- and context-aware design in practice. We conclude with overarching lessons learned that carry into constructive opportunities for future research.