As machine learning models are increasingly used to make consequential decisions involving humans, it is important that the models do not discriminate on the basis of protected attributes such as race and gender. However, the model holder are not the people who bear the brunt of the harm from a discriminatory model, so there is little natural incentive for the model holder to fix a discriminatory model. It would thus be societally beneficial if other entities could also detect or mitigate unfair behavior in these models. Black-box methods, which require only query access to the model, are well suited for this purpose, as they are feasible to carry out without knowing the full details of the model.

In this thesis, I consider three different forms of unfairness and present black-box methods that address them. The first of the three is proxy use, where some component of a model is a proxy for a protected attribute. The second is the lack of individual fairness, which formalizes the intuitive notion that a model should not make arbitrary decisions. Finally, a model may have an unrepresentative training set, which can lead the model to exhibit varying degrees of accuracy for different protected groups. For each of these behaviors, I propose one or more methods that can help detect such behavior in models or ensure the lack thereof. These methods require only black-box access to the model, allowing them to be effective even when the model holder is uncooperative. My theoretical and experimental analysis of these methods evidence their effectiveness in this setting, showing that they are useful technical tools that can support an effective response against discrimination.

101 pages

Thesis Committee:
Matt Fredrikson (Chair)
Alexandra Chouldechova
Anupam Datta
Ariel Procaccia
Sharad Goel (Stanford University)

Srinivasan Seshan, Head, Computer Science Department
Martial Hebert, Dean, School of Computer Science

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