Evaluating Fairness in Information Retrieval Systems: A Study on the Performance of the FAIR Metric
Year: 2023
Type: dissertation
Abstract: In the digital era, the use of information retrieval (IR) technologies has surged, enabling users to access vast amounts of data quickly. However, concerns have arisen regarding bias and unfairness in these systems, leading to unequal treatment and outcomes for different user groups, such as racial, gender, or socioeconomic bias. To ensure equitable access to information, it is crucial to establish a fair IR system. \n\nThis thesis focuses on the FAIR metric proposed by Gao et al, 2022 and investigates its performance by replacing standard information retrieval (IR) utility metrics. It explores the impact of different metrics on the overall performance of FAIR, providing a comprehensive analysis of its effectiveness in evaluating fairness in IR systems.
Author: Eriksen, Maria
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W7047172940