Semester of Graduation

Spring 2022


Master of Science in Computer Science (MSCS)


The Division of Computer Science and Engineering

Document Type



Online Professor Reputation (OPR) systems, such as (RMP), are frequently used by college students to post and access peer evaluations of their pro- fessors. However, recent evidence has shown that these platforms suffer from major bias problems. Failing to address bias in online professor ratings not only leads to negative expectations and experiences in class, but also poor performance on exams. To address these concerns, in this thesis, we study bias in OPR systems from a software design point of view. At the first phase of our analysis, we conduct a systematic literature review of 23 interdisciplinary studies on bias problems affecting OPR systems. Our objective is to systematically categorize and synthesize existing evidence and identify features of OPR systems which enable offline patterns of bias to flourish online. In the second phase, we propose several preventive and corrective software design strategies to mitigate bias in OPR systems. Our objective is to highlight evidence-based design tactics that software engineers can use to develop OPR systems that are immune to bias by design.

Committee Chair

Mahmoud, Anas