Friday, July 9, 2021 - 3:00pm to 5:00pm
Location:Virtual Presentation - ET Remote Access - Zoom
Speaker:ANSON HAN KAHNG, Ph.D. Student https://www.cs.cmu.edu/~akahng/
Computational Perspectives on Democracy
Democracy is a natural approach to large-scale decision-making that allows people affected by a potential decision to provide input about the outcome. However, modern implementations of democracy are based on outdated information technology and must adapt to the changing technological landscape. This thesis explores the relationship between computer science and democracy, which is, crucially, a two-way street—just as principles from computer science can be used to analyze and design democratic paradigms, ideas from democracy can be used to solve hard problems in computer science.
Question 1: What can computer science do for democracy?
To explore this first question, we examine the theoretical foundations of three democratic paradigms: liquid democracy, participatory budgeting, and multiwinner elections. Each of these paradigms broadly redistributes power from the few to the many: For instance, liquid democracy allows people to choose delegates more flexibly and participatory budgeting enables citizens to directly influence government spending toward public projects. However, because these paradigms are relatively new, their theoretical properties are relatively unexplored. We analyze each of these three settings from the point of view of computational social choice with a focus on robustness, fairness, and efficiency in order to provide actionable advice for future iterations of these paradigms.
Question 2: What can democracy do for computer science?
Toward this end, we explore two settings in which democratic principles can be used to augment approaches to making difficult decisions—in our case, automating ethical decision-making and hiring in online labor markets. Both of these problems are difficult in the sense that there is no universally agreed-upon function to optimize, making them a poor fit for traditional approaches in computer science. Instead, we try to emulate a world in which we can get input from people in order to arrive at a “societal” decision. In each of these settings, we first propose and analyze a theoretical approach that leads to principled decisions, and then, in collaboration with HCI researchers, run experiments in the real world to test the efficacy and practicability of our approaches in the real world.
Ariel Procaccia (Chair)
David Pennock (Rutgers University)
Vincent Conitzer (Duke University)
Zoom Participation. See announcement.