Incorporating Fairness in Paper Recommendation

Although many conferences use double-blind reviewing to increase fairness, studies show that bias still occurs. Our research focuses on developing fair algorithms that correct for these biases and select papers from a more demographically diverse group of authors. To increase author diversity and achieve demographic parity, we use multidimensional author profiles with Boolean feature values, i.e., gender, ethnicity, career stage, university rank, and geolocation. Based on these profiles, we present two algorithms that explicitly consider demographic diversity and paper quality during paper recommendation. To evaluate our approaches, we compare the resulting set of conference papers with the actual accepted papers in the conference, measuring the diversity gain, utility savings, and F-measure for each method. Our best method, Multi-Faceted Diversity, produces a set of papers whose authors achieve 95% similarity to the demographics of the pool across multiple dimensions, increasing the selected papers' authors by 46% with only a 2.48% drop in utility. Tasks within academia, such as conference papers, journal papers, grant and proposal reviews, could benefit from applying this approach.