Year: 2025
Type: conference-paper
Abstract: Fairness-aware information retrieval (IR) has attracted growing attention, with numerous metrics and algorithms proposed. However, the complexity of fairness and IR systems makes it challenging to systematically summarize progress and identify future directions. Economics has long studied fairness and offers a system-oriented perspective that naturally captures societal and intertemporal trade-offs. In this tutorial, we first frame IR systems as specialized economic markets and reorganize fairness algorithms along three key economic dimensions: macro vs. micro, demand vs. supply, and short-term vs. long-term. Unlike prior fairness-aware tutorials, this economic lens not only provides a structured reframing of fairness-aware IR, but also points toward new opportunities by encouraging the use of economic tools to address open problems.
Institutions: Renmin University of China, University of Amsterdam
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W4412378283