  Personalization is pervasive in the online space as it leads to higher
efficiency and revenue by allowing the most relevant content to be served to
each user. However, recent studies suggest that personalization methods can
propagate societal or systemic biases and polarize opinions; this has led to
calls for regulatory mechanisms and algorithms to combat bias and inequality.
Algorithmically, bandit optimization has enjoyed great success in learning user
preferences and personalizing content or feeds accordingly. We propose an
algorithmic framework that allows for the possibility to control bias or
discrimination in such bandit-based personalization. Our model allows for the
specification of general fairness constraints on the sensitive types of the
content that can be displayed to a user. The challenge, however, is to come up
with a scalable and low regret algorithm for the constrained optimization
problem that arises. Our main technical contribution is a provably fast and
low-regret algorithm for the fairness-constrained bandit optimization problem.
Our proofs crucially leverage the special structure of our problem. Experiments
on synthetic and real-world data sets show that our algorithmic framework can
control bias with only a minor loss to revenue.
