Robust reputation independence in ranking systems for multiple sensitive attributes

• Published in: Machine learning Vol. 111; no. 10; pp. 3769 - 3796
• Main Authors: Ramos, Guilherme, Boratto, Ludovico, Marras, Mirko
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Computer Science; Control; Demographics; Machine Learning; Mechatronics; Natural Language Processing (NLP); Ranking; Reputations; Robotics; Robustness; Simulation and Modeling; Special Issue: Foundations of Data Science; Ranking systems; Bias mitigation; Robustness; Reputation
• ISSN: 0885-6125; 1573-0565
• DOI: 10.1007/s10994-022-06173-0

Ranking systems have an unprecedented influence on how and what information people access, and their impact on our society is being analyzed from different perspectives, such as users’ discrimination. A notable example is represented by reputation-based ranking systems, a class of systems that rely on users’ reputation to generate a non-personalized item-ranking, proved to be biased against certain demographic classes. To safeguard that a given sensitive user’s attribute does not systematically affect the reputation of that user, prior work has operationalized a reputation independence constraint on this class of systems. In this paper, we uncover that guaranteeing reputation independence for a single sensitive attribute is not enough. When mitigating biases based on one sensitive attribute (e.g., gender), the final ranking might still be biased against certain demographic groups formed based on another attribute (e.g., age). Hence, we propose a novel approach to introduce reputation independence for multiple sensitive attributes simultaneously. We then analyze the extent to which our approach impacts on discrimination and other important properties of the ranking system, such as its quality and robustness against attacks. Experiments on two real-world datasets show that our approach leads to less biased rankings with respect to multiple users’ sensitive attributes, without affecting the system’s quality and robustness.