Fairness and the Public Good in Social Networks with Strategic Actors

近年来，用于决策的人工智能(AI)算法发展迅速。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的风险决策。随之而来的是一种迫切的需求，即确保这些算法的决定对各方都是公平和公平的，并以公共利益为目标。对“公平”和“公共利益”的解释有很多种。这一建议侧重于多智能体系统和博弈论中的公平概念。多智能体系统是对多个智能体社区的研究，每个智能体通常根据自己的利益独立行动。博弈论是在其他战略代理人在场的情况下进行战略决策的数学。综上所述，这些领域为AI算法运行的物理和数字空间提供了丰富的模型。它们还提供了公平的概念，表征了算法的益处或公平程度--例如，当代理人没有提供虚假报告的动机时，算法可能是“真实的”；或者，结果可能处于“纳什均衡”，代理人合理地满意他们不想改变他们的行动。我们专注于研究和应用这些公平概念的一个特定领域--社会网络。在社会层面上，社交网络(以社交媒体平台的形式)触及了我们生活的许多方面。他们收集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符合公共利益。它们还控制着信息在数字空间的传播，因此既可以传播有用的信息，也可以传播有害的虚假信息。这项提案研究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，从而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.