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.

翻译：算法公平性是人工智能融入社会后的一项基本要求。在人工智能分配资源的社交应用中，算法往往需要做出使部分用户受益的决策，有时甚至是重复或排他性地受益，同时试图最大化特定结果。我们应如何设计此类系统以更公平地服务用户？本文以一款名为Step Heroes的社交运动游戏中用户群体共同努力实现共同目标的情形为例，探讨了这一问题。我们识别了传统多臂老虎机（MABs）中的不良结果，并形式化了贪婪老虎机问题。随后，我们提出了一种基于新型公平感知多臂老虎机——Shapley Bandits的解决方案。该方法利用夏普利值提升整体玩家的参与度和干预依从性，而非像传统方式那样仅通过偏袒高绩效参与者来最大化群体总产出。我们通过一项用户研究（n=46）评估了该方法。结果表明，我们的Shapley Bandits能有效缓解贪婪老虎机问题，并在参与者中实现更好的用户留存率和动机提升。