  Algorithmic fairness is an essential requirement as AI becomes integrated in
society. In the case of social applications where AI distributes resources,
algorithms often must make decisions that will benefit a subset of users,
sometimes repeatedly or exclusively, while attempting to maximize specific
outcomes. How should we design such systems to serve users more fairly? This
paper explores this question in the case where a group of users works toward a
shared goal in a social exergame called Step Heroes. We identify adverse
outcomes in traditional multi-armed bandits (MABs) and formalize the Greedy
Bandit Problem. We then propose a solution based on a new type of
fairness-aware multi-armed bandit, Shapley Bandits. It uses the Shapley Value
for increasing overall player participation and intervention adherence rather
than the maximization of total group output, which is traditionally achieved by
favoring only high-performing participants. We evaluate our approach via a user
study (n=46). Our results indicate that our Shapley Bandits effectively
mediates the Greedy Bandit Problem and achieves better user retention and
motivation across the participants.
