by Tim de Jonge and Djoerd Hiemstra
Modern society increasingly relies on Information Retrieval systems to answer various information needs. Since this impacts society in many ways, there has been a great deal of work to ensure the fairness of these systems, and to prevent societal harms. There is a prevalent risk of failing to model the entire system, where nefarious actors can produce harm outside the scope of fairness metrics. We demonstrate the practical possibility of this risk through UNFair, a ranking system that achieves performance and measured fairness competitive with current state-of-the-art, while simultaneously being manipulative in setup. UNFair demonstrates how adhering to a fairness metric, Amortized Equity, can be insufficient to prevent Search Engine Manipulation. This possibility of manipulation bypassing a fairness metric discourages imposing a fairness metric ahead of time, and motivates instead a more holistic approach to fairness assessments.
To be presented at the ACM Conference on Fairness, Accountability, and Transparency (FAccT 2023) on 12-15 June in Chicago, USA.