Document Type
Article
Publication Date
2021
Keywords
User Profiling; Paper Recommendation; Diversity and Fairness
Abstract
Despite double-blind peer review, bias affects which papers are selected for inclusion in conferences and journals. 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 during conference papers, journal papers, or grant proposal selection or other tasks within academia.
Citation
Alsaffar, R. and Gauch, S. (2021). Multidimensional Demographic Profiles for Fair Paper Recommendation. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KDIR; ISBN 978-989-758-533-3; ISSN 2184-3228, SciTePress, pages 199-208. DOI: 10.5220/0010655800003064
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
