Fairness and the Public Good in Social Networks with Strategic Actors

近年来，人工智能（AI）算法在决策方面的发展激增。这些日益复杂的算法利用不断增长的信息存储来做出越来越高风险的决策。随着这种转变，迫切需要确保这些算法的决定对所有相关方都是公平公正的，并以更大的公众利益为导向。对“公平”和“公共利益”有多种解释。这一建议侧重于多智能体系统和博弈论的公平概念。多智能体系统是研究每个独立行动的智能体群体，通常根据自己的利益。博弈论是在其他战略主体存在的情况下进行战略决策的数学。综合起来，这些领域提供了人工智能算法运行的物理和数字空间的丰富模型。它们还提供了公平概念，表征算法的有益或公平程度——例如，当代理没有提供虚假报告的动机时，算法可能是“真实的”；或者，结果可能是“纳什均衡”，在这种均衡中，行为主体合理地满足于他们不想改变自己的行为。我们专注于研究和应用这些公平概念的一个特定领域——社交网络。在社会层面上，社交网络（以社交媒体平台的形式）触及我们生活的许多方面。他们收集了大量关于我们的信息，并利用这些信息来驱动人工智能算法，这些算法可能会也可能不会为公共利益服务。他们还控制着数字空间中信息的传播，因此，他们既可以调解有用的信息，也可以调解有害的虚假信息。本提案探讨了我们如何在社交网络环境中最好地利用人工智能算法来实现公共利益，为所有参与者实现公平和公平的结果。

Recent years saw a surge of development of artificial intelligence (AI) algorithms for decision making. These increasingly sophisticated algorithms make use of the ever-growing stores of information to make increasingly high stakes decisions. With this shift comes a pressing need to ensure that the decisions of these algorithms are fair and equitable to all parties involved, and are directed toward the public greater good. There are many ways to interpret "fairness" and "public good". This proposal focuses on fairness notions from multiagent systems and game theory. Multiagent systems is the study of communities of agents each acting independently, often according to its own interests. Game theory is the mathematics of strategic decision-making in the presence of other strategic agents. Taken together, these fields provide a rich model of the physical and digital spaces in which AI algorithms operate. They also provide fairness notions that characterize how beneficial or equitable algorithms are - for instance, an algorithm may be "truthful" where agents have no incentives to provide false reports; or, the outcome may be at a "Nash equilibrium" where agents are reasonably satisfied that they would not want to alter their actions. We focus on a particular domain for the research and application of these fairness notions - social networks. On a societal level, social networks (in the form of social media platforms) touch many aspects of our lives. They gather vast amounts of information about us and use this information to drive AI algorithms that may or may not be acting in the interest of the public good. They also control the propagation of information in digital spaces, and therefore are in a position to mediate both useful messages as well as harmful disinformation. This proposal examines how we can best leverage AI algorithms in a social network environment toward the public good, toward achieving fair and equitable outcomes for all participants.