Bryan Wilder: Group-Fairness in Influence Maximization скачать в хорошем качестве

Bryan Wilder: Group-Fairness in Influence Maximization

Bryan Wilder, Harvard Mar 6, 2020 Title: Group-Fairness in Influence Maximization CompSust Open Graduate Seminar (COGS) http://www.compsust.net/cogs.php Abstract: Influence maximization is a widely used model for information dissemination in social networks. Recent work has employed such interventions across a wide range of social problems, spanning public health, substance abuse, and international development (to name a few examples). A critical but understudied question is whether the benefits of such interventions are fairly distributed across different groups in the population; e.g., avoiding discrimination with respect to sensitive attributes such as race or gender. Drawing on legal and game-theoretic concepts, we introduce formal definitions of fairness in influence maximization. We provide an algorithmic framework to find solutions which satisfy fairness constraints, and in the process improve the state of the art for general multi-objective submodular maximization problems. Experimental results on real data from an HIV prevention intervention for homeless youth show that standard influence maximization techniques oftentimes neglect smaller groups which contribute less to overall utility, resulting in a disparity which our proposed algorithms substantially reduce. Bio: Bryan Wilder is a fifth-year PhD student in Computer Science at Harvard University, where he is advised by Milind Tambe. His research focuses on the intersection of optimization and machine learning, with the goal of improving interventions that serve vulnerable populations. His work has received or been nominated for best paper awards at ICML and AAMAS, and was a finalist for the INFORMS Doing Good with Good OR competition. He is supported by a NSF Graduate Research Fellowship.