  Contextual bandit algorithms are at the core of many applications, including
recommender systems, clinical trials, and optimal portfolio selection. One of
the most popular problems studied in the contextual bandit literature is to
maximize the sum of the rewards in each round by ensuring a sublinear regret
against the best-fixed context-dependent policy. However, in many applications,
the cumulative reward is not the right objective - the bandit algorithm must be
fair in order to avoid the echo-chamber effect and comply with the regulatory
requirements. In this paper, we consider the $\alpha$-Fair Contextual Bandits
problem, where the objective is to maximize the global $\alpha$-fair utility
function - a non-decreasing concave function of the cumulative rewards in the
adversarial setting. The problem is challenging due to the non-separability of
the objective across rounds. We design an efficient algorithm that guarantees
an approximately sublinear regret in the full-information and bandit feedback
settings.
