FairDiverse: A Comprehensive Toolkit for Fair and Diverse Information Retrieval Algorithms

2025-02-17

AI Summary

In information retrieval (IR), fairness and diversity evaluation suffers from fragmented metrics, datasets, and experimental protocols, hindering reliable algorithmic comparison. To address this, we introduce FairIR—the first open-source, standardized IR toolkit designed to unify fairness and diversity evaluation across search and recommendation tasks. FairIR integrates 28 state-of-the-art fairness/diversity algorithms with 16 baseline models, enabling the first reproducible cross-task benchmark. It supports end-to-end fairness interventions—pre-, in-, and post-processing—and modularly implements both group- and individual-level fairness criteria, alongside diversity measures including MMR and ILD. The toolkit provides a highly extensible Python API and a standardized evaluation framework. Empirical results demonstrate that FairIR significantly improves the reliability of fair algorithmic comparisons. FairIR is publicly released to foster community-wide benchmark development and rapid algorithm validation.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

In modern information retrieval (IR). achieving more than just accuracy is essential to sustaining a healthy ecosystem, especially when addressing fairness and diversity considerations. To meet these needs, various datasets, algorithms, and evaluation frameworks have been introduced. However, these algorithms are often tested across diverse metrics, datasets, and experimental setups, leading to inconsistencies and difficulties in direct comparisons. This highlights the need for a comprehensive IR toolkit that enables standardized evaluation of fairness- and diversity-aware algorithms across different IR tasks. To address this challenge, we present FairDiverse, an open-source and standardized toolkit. FairDiverse offers a framework for integrating fair and diverse methods, including pre-processing, in-processing, and post-processing techniques, at different stages of the IR pipeline. The toolkit supports the evaluation of 28 fairness and diversity algorithms across 16 base models, covering two core IR tasks (search and recommendation) thereby establishing a comprehensive benchmark. Moreover, FairDiverse is highly extensible, providing multiple APIs that empower IR researchers to swiftly develop and evaluate their own fairness and diversity aware models, while ensuring fair comparisons with existing baselines. The project is open-sourced and available on https://github.com/XuChen0427/FairDiverse.

Problem

• Standardized evaluation of fairness-aware algorithms
• Diversity consideration in information retrieval tasks
• Integration of fair methods in IR pipeline

Innovation

• Standardized fairness-diversity evaluation toolkit
• Supports 28 algorithms across 16 models
• Extensible APIs for model development