Economics for Fairness

Tutorial Series

Fairness in Information Retrieval from an Economic Perspective

A tutorial series on fairness in information retrieval through an economic lens, connecting algorithmic design, stakeholder trade-offs, and long-term societal impact.

Download ICMR/ECIR PDF Download SIGIR 2025 Slides
  • Tutorial series across SIGIR 2025, ECIR 2026, and ICMR 2026
  • Slides, toolkit links, and updates for SIGIR, ECIR, and ICMR
  • Book manuscript currently in progress
New

ICMR / ECIR Tutorial Material

The latest combined tutorial slides are now available as icmr_ecir_tutorial.pdf.

Open the PDF
Book in Progress

We are currently writing a book on fairness in information retrieval from an economic perspective, and we will share updates here as the manuscript progresses.

Overview

Why economics helps us study fairness in IR

Fairness-aware information retrieval systems have received growing attention in recent years. At the same time, the space of fairness notions, metrics, and interventions has become broader and harder to navigate, especially once we consider users, platforms, providers, and long-term effects together.

Our tutorial frames information retrieval systems as specialized economic markets. From that perspective, we revisit fairness through three dimensions: macro versus micro, demand versus supply, and short-term versus long-term outcomes. This lens helps connect existing algorithmic work with broader societal and intertemporal trade-offs.

The tutorial aims to provide a clear conceptual map of the area, highlight open questions, and encourage the use of economic tools and insights in future fairness-aware IR research and practice.

Materials

Tutorial slides and resources

SIGIR 2025 Slides

Download The SIGIR 2025 tutorial deck remains available for reference.

Open SIGIR slides

Tutorial Proposal

Reference The SIGIR 2025 proposal provides the motivation and scope of the tutorial.

Open proposal Proposal DOI

Intersection

Where economics meets information retrieval

Economic Incentives

Markets, welfare, exposure, and allocation help explain how IR systems shape outcomes across stakeholders.

IR Pipelines

Search and recommendation systems operationalize these trade-offs through ranking, retrieval, and re-ranking decisions.

Shared Lens

The tutorial studies fairness through a joint lens of utility, demand, supply, and long-term ecosystem dynamics.

Toolkit

FairDiverse code, paper, and tutorial links

FairDiverse Toolkit

GitHub FairDiverse is an open-source toolkit for fairness- and diversity-aware information retrieval, supporting search and recommendation pipelines.

Open the FairDiverse repository

Citation

Reference this tutorial and toolkit

FairDiverse BibTeX

@inproceedings{xu2025fairdiverse,
  author    = {Chen Xu and Zhirui Deng and Clara Rus and Xiaopeng Ye and
               Yuanna Liu and Jun Xu and Zhicheng Dou and Ji-Rong Wen and
               Maarten de Rijke},
  title     = {FairDiverse: A Comprehensive Toolkit for Fair and Diverse
               Information Retrieval Algorithms},
  booktitle = {Proceedings of the 48th International ACM SIGIR Conference
               on Research and Development in Information Retrieval},
  year      = {2025},
  doi       = {10.1145/3726302.3730280},
  url       = {https://doi.org/10.1145/3726302.3730280}
}

Tutorial Reference

@inproceedings{xu2025economicfairness,
  author    = {Chen Xu and Clara Rus and Yuanna Liu and Marleen de Jonge and
               Jun Xu and Maarten de Rijke},
  title     = {Fairness in Information Retrieval from an Economic Perspective},
  booktitle = {Proceedings of the 48th International ACM SIGIR Conference
               on Research and Development in Information Retrieval},
  year      = {2025},
  pages     = {4126--4129},
  doi       = {10.1145/3726302.3731694},
  url       = {https://doi.org/10.1145/3726302.3731694}
}

You can cite the tutorial proposal directly, and use the FairDiverse paper for the associated toolkit.

Related Materials

Papers, surveys, and background readings

Economic IR

P-MMF: Provider Max-min Fairness Re-ranking in Recommender System

Chen Xu, Sirui Chen, Jun Xu, Weiran Shen, Xiao Zhang, Gang Wang, Zhenhua Dong. WWW 2023. Spotlight / Best Paper Nomination.

DOI Author page
Economic IR

A Taxation Perspective for Fair Re-ranking

Chen Xu, Xiaopeng Ye, Wenjie Wang, Liang Pang, Jun Xu, Tat-Seng Chua. SIGIR 2024. Best Paper Honorable Mention.

DOI arXiv
Economic IR

Understanding Accuracy-Fairness Trade-offs in Re-ranking through Economic Theory

Chen Xu, Xiaopeng Ye, Clara Rus, Yuanna Liu, Jun Xu, Maarten de Rijke. SIGIR 2025.

DOI arXiv
Economic IR

The Attention Market: Interpreting Online Fair Re-ranking as Manifold Optimization under Walrasian Equilibrium

Chen Xu, Wei Chu, Wenyu Hu, Fengran Mo, Jun Xu, Maarten de Rijke. arXiv 2026.

arXiv Tutorial context
IR Systems

FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval

Chen Xu, Jun Xu, Yiming Ding, Xiao Zhang, Qi Qi. WWW 2024.

DOI arXiv
Survey

Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era

Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, Jun Xu. KDD 2024 tutorial / survey material.

DOI arXiv
Survey

A Survey on the Fairness of Recommender Systems

Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, Shaoping Ma. TOIS 2022.

DOI arXiv
Survey

Fairness and Diversity in Recommender Systems: A Survey

Recent survey covering fairness and diversity together in recommender systems.

DOI
Survey

Fairness in Ranking, Part II: Learning-to-Rank and Recommender Systems

Foundational survey on fairness in ranking and recommendation settings.

DOI
Survey Materials

LLM-IR Bias/Fairness Survey Repository

Paper collection, tutorial links, and a continuously updated reading list for bias and fairness in IR with LLMs.

GitHub Tutorial site

Venues

Conference websites

SIGIR 2025

The conference website for the original tutorial edition.

Visit SIGIR 2025

ECIR 2026

Official website for the European Conference on Information Retrieval.

Visit ECIR

ICMR 2026

Official website for the ACM International Conference on Multimedia Retrieval.

Visit ICMR

People

Tutorial organizers