Funding Source: NSF CRII Award Number (FAIN): 2348275 – Computers now play a role in many kinds of voting systems and even make new ones possible. For example, online liquid democracy platforms allow users to vote directly on a topic or delegate their vote to trusted proxies whom they believe are more informed and represent their interests. However, recent audits of machine learning and artificial intelligence systems demonstrate that algorithmic decisions made by computers can unintentionally disadvantage individuals or groups of people. This has driven the study of algorithmic fairness, the design of algorithms that guarantee some notion of fairness or equity.
This project will investigate computational social choice through an algorithmic fairness lens and illuminate the theoretical limitations of achieving difficult or conflicting concepts of fairness. The short term goal is to formalize and study algorithmic fairness problems in the computational social choice context. The long term goal is to continue developing a subfield addressing issues of fairness in any algorithm that touches a democratic process whether through the implementation or analysis of the system.
Faculty: Brian Brubach
Department: Computer Science
Funding Source: NSF CRII Award Number (FAIN): 2348275