Talk by Ana-Andreea Stoica

Ranking algorithms have recently come under scrutiny for preventing minority groups from reaching higher ranking slots in applications like search and recommendation, thus reducing their visibility. In this talk, I will describe our recent work in diagnosing when and how algorithms that use network information may further bias against minority groups. We focus on two famous algorithms, PageRank and HITS, and analyze them empirically and theoretically, using a generative network model with multiple communities. We find that HITS amplifies pre-existing bias in homophilic networks, as compared to PageRank. We find the root cause of bias amplification in HITS to be the level of homophily present in the network. This work is joint with Augustin Chaintreau and Nelly Litvak and was published at The Web Conference ’24 (https://dl.acm.org/doi/pdf/10.1145/3589334.3645609).

Zoom Link: https://bit.ly/LSNI-2024