Date of Graduation
12-2022
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science (PhD)
Degree Level
Graduate
Department
Computer Science & Computer Engineering
Advisor/Mentor
Gauch, Susan E.
Committee Member
Panda, Brajendra N.
Second Committee Member
Andrews, David L.
Third Committee Member
Kent, Laura B.
Keywords
Data Mining; Fairness and diversity; Information Retrieval; Recommendation System; Text Mining
Abstract
Peer review is the process by which publishers select the best publications for inclusion in a journal or a conference. Bias in the peer review process can impact which papers are selected for inclusion in conferences and journals. Although often implicit, race, gender and other demographics can prevent members of underrepresented groups from presenting at major conferences. To try to avoid bias, many conferences use a double-blind review process to increase fairness during reviewing. However, recent studies argue that the bias has not been removed completely. Our research focuses on developing fair algorithms that correct for these biases and select papers from a more demographically diverse group of authors. To address this, we present fair algorithms that explicitly incorporate author diversity in paper recommendation using multidimensional author profiles that include five demographic features, i.e., gender, ethnicity, career stage, university rank, and geolocation. The Overall Diversity method ranks papers based on an overall diversity score whereas the Multifaceted Diversity method selects papers that fill the highest-priority demographic feature first. We evaluate these algorithms with Boolean and continuous-valued features by recommending papers for SIGCHI 2017 from a pool of SIGCHI 2017, DIS 2017 and IUI 2017 papers and compare the resulting set of papers with the papers accepted by the conference. Both methods increase diversity with small decreases in utility using profiles with either Boolean or continuous feature values. Our best method, Multifaceted Diversity, recommends a set of papers that match demographic parity, selecting authors who are 42.50% more diverse with a 2.45% gain in utility. This approach could be applied when selecting conference papers, journal papers, grant proposals, or other tasks within academia.
Citation
Alsaffar, R. (2022). Multivariate Fairness for Paper Selection. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/4797
Included in
Gender, Race, Sexuality, and Ethnicity in Communication Commons, Graphics and Human Computer Interfaces Commons, Information Security Commons, Race and Ethnicity Commons, Theory and Algorithms Commons
