  Contextual bandit algorithms have become widely used for recommendation in
online systems (e.g. marketplaces, music streaming, news), where they now wield
substantial influence on which items get exposed to the users. This raises
questions of fairness to the items -- and to the sellers, artists, and writers
that benefit from this exposure. We argue that the conventional bandit
formulation can lead to an undesirable and unfair winner-takes-all allocation
of exposure. To remedy this problem, we propose a new bandit objective that
guarantees merit-based fairness of exposure to the items while optimizing
utility to the users. We formulate fairness regret and reward regret in this
setting, and present algorithms for both stochastic multi-armed bandits and
stochastic linear bandits. We prove that the algorithms achieve sub-linear
fairness regret and reward regret. Beyond the theoretical analysis, we also
provide empirical evidence that these algorithms can fairly allocate exposure
to different arms effectively.
