Auditing biases of algorithmic academic rankings
Stuti Dewan
Undergraduate Researcher
Computer Science Major (Luddy School of Informatics, Computing, & Engineering)
YY Ahn
Faculty Mentor
YY Ahn (Luddy School of Informatics, Computing and Engineering)
Project Description
In this project, we will examine the biases in csrankings.org, an algorithmic ranking system for ranking computer science departments based on publications. Understanding issues with algorithmic ranking is critical because ranking, or prestige, dictates a lot of incentives in academia and thus can inflict severe damage on the higher education system. For instance, rankings drive students, which in turn affect faculty hiring decisions, as well as what kinds of science are done at the universities. Here, we will audit the data and algorithm used in the algorithmic ranking to analyze the potential biases in the ranking.
Technology or Computational Component
The project will require examining algorithms implemented in the ranking systems as well as analyzing databases used by the system.