  Online platforms often face challenges being both fair (i.e.,
non-discriminatory) and efficient (i.e., maximizing revenue). Using computer
vision algorithms and observational data from a micro-lending marketplace, we
find that choices made by borrowers creating online profiles impact both of
these objectives. We further support this conclusion with a web-based
randomized survey experiment. In the experiment, we create profile images using
Generative Adversarial Networks that differ in a specific feature and estimate
its impact on lender demand. We then counterfactually evaluate alternative
platform policies and identify particular approaches to influencing the
changeable profile photo features that can ameliorate the fairness-efficiency
tension.
