UC Berkeley

From digital platforms, automated transportation to healthcare, the rapid deployment of machine learning has in many ways changed our everyday life. However, when learning systems are deployed in the real world, they immediately face a complex social and economic context which poses feasibility constraints, drives the underlying dynamics, and influences the kinds of data that the systems can actually obtain. Optimizing a single offline objective in isolation to these contexts can lead to severe unintended consequences at deployment and hinder the improvement of social welfare that the system has the potential to bring.

In this thesis, I summarize my research works on developing learning algorithms that incorporate such social economic contexts into the design from three aspects: (i) Learning with noisy input data; (ii) Learning with bandit-type user feedback; (iii) Learning under causal dynamics. I will situate each of these with the particular applications of machine learning on fair classification, resource allocation, auction and platform design.