Scalable Assessment and Mitigation Strategies for Fairness in Rankings
Year: 2020
Type: preprint
Abstract: Motivated by industrial-scale applications, we consider two specific areas of fairness, one connected to the notion of equality of opportunity, and the other one generally tied to fair model performance. Throughout the paper, we consider only methods that can be scaled to Internet-industry size datasets. With this in mind, we propose a simple post-processing method to achieve equality of opportunity and discuss challenges and some solutions in the specific cases of recommendation systems and rankings. We then discuss a class of model performance fairness measures based on conditional ROC curves. We propose both scalable uncertainty assessment tools (that improve upon recent research) as well as scalable penalized methods to improve fairness with respect to these metrics. We provide fast algorithms with an emphasis on making few passes over the data when possible.
Source: arXiv (Cornell University)
Authors: Preetam Nandy, Amir Sepehri, Kinjal Basu, Heloise Logan, Deepak Agarwal, Noureddine El Karoui
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W3036595270