Publications

You can also find my articles on my Google Scholar profile.

Judiciously Reducing Sub-group Comparisons for Learning Intersectional Fair Representations

Clara Rus, Andrew Yates, Maarten de Rijke

Published in ECIR 2026, 2025

Ensuring fairness in ranking systems is critical to avoid discriminatory outcomes towards minority groups in high stakes domains such as recruitment. Most fairness interventions only address fairness for one or more binary groups without accounting for intersectional fairness. We study the problem of achieving intersectional fairness in ranking systems, where individuals may face compounded disadvantages. We adapt and extend pre-processing fairness intervention methods to optimize for intersectional group fairness. As the number of intersectional sub-groups grows exponentially with the number of attributes, optimization becomes computationally expensive. We propose to reduce the number of sub-group comparisons when optimizing for intersectional fairness, based on the highest disparities between sub-groups. We show that limiting sub-group comparisons achieves comparable or better intersectional fairness. We validate this on three real-world datasets and a simulated setup designed to test robustness to intersectional fairness challenges.

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Joint Modeling of Candidate and Recruiter Preferences for Fair Two-Sided Job Matching

Clara Rus, Masoud Mansoury, Andrew Yates, Maarten de Rijke

Published in ECIR2026, 2025

Recommender systems in recruitment platforms involve two active sides, candidates and recruiters, each with distinct goals and preferences. Most recommendation methods address only one side of the problem, leading to potentially ineffective matches. We propose a two-sided fusion framework that jointly models candidate and recruiter preferences to enhance mutual matches between candidates and recruiters. We also propose a personalized two-sided fusion approach to enhance the fairness of job recommendations. Experiments on the XING recruitment dataset show that the proposed approach improves fairness and compatibility, demonstrating the benefits of incorporating two-sided preferences in fairness-aware recommendations.

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Does fair ranking lead to fair recruitment outcomes? A study of interventions, interfaces, and interactions.

Alessandro Fabris, Clara Rus, Jorge Saldivar, Anna Gatzioura, Asia Biega, Carlos Castillo

Published in IPM Journal, 2025

Personnel recruitment is increasingly mediated by Applicant Tracking Systems (ATS), which rank candidates for job positions, making them a central decision-support tool in modern HR processes. Often framed as an information retrieval (IR) problem, the ranking of candidates in ATS is typically driven by relevance to the job position, with algorithms sorting applicants according to a set of predefined criteria. In recent years, fairness-aware ranking methods have emerged to mitigate the risk of indirect discrimination, where the ordering of candidates may inadvertently favor one demographic group over another. These approaches are inspired by browsing models developed for web search and aim to balance candidate exposure based on protected characteristics. However, ATS in recruitment introduce unique challenges due to their high-stakes nature and the decision-making context in which they operate. In this paper, we present a series of user studies that explore the disconnect between fair exposure and fair outcomes in candidate shortlisting. We focus on how factors such as task design (e.g., how recruiters interact with candidate lists), individual representations of candidates (e.g., national origin cues), and ranking order influence both position bias and demographic balance. Our findings show that while demographic balance may be achieved in terms of ranking visibility, this does not necessarily translate to fair outcomes in terms of who gets shortlisted. Through a crowdsourced experiment and in-depth interviews with recruiters, we identify key task-level, individual, and ranking factors that mediate these effects. We conclude that fairness in ATS rankings is contingent not only on algorithmic design but also on the shortlisting tasks they support, as well as the interfaces, strategies, and assumptions that recruiters use when interacting with candidate lists. Based on these insights, we provide implications for the design of algorithms, interfaces, and recruitment processes that support fairer and more equitable recruitment outcomes.

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Fairness in information retrieval from an economic perspective

Chen Xu, Clara Rus, Yuanna Liu, Marleen de Jonge, Jun Xu, Maarten de Rijke

Published in SIGIR, 2025

Recently, fairness-aware information retrieval (IR) systems have been receiving much attention. Numerous fairness metrics and algorithms have been proposed. The complexity of fairness and IR systems makes it challenging to provide a systematic summary of the progress that has been made. This complexity calls for a more structured framework to navigate future fairness-aware IR research directions. The field of economics has long explored fairness, offering a strong theoretical and empirical foundation. Its system-oriented perspective enables the integration of IR fairness into a broader framework that considers societal and intertemporal trade-offs. In this tutorial, we first highlight that IR systems can be understood as a specialized economic market. Then, we re-organize fairness algorithms through three key economic dimensions—macro vs. micro, demand vs. supply, and short-term vs. long-term. We effectively view most fairness categories in IR from an economic perspective. Finally, we illustrate how this economic framework can be applied to various real-world IR applications and we demonstrate its benefits in industrial scenarios. Different from other fairness-aware tutorials, our tutorial not only provides a new and clear perspective to re-frame fairness-aware IR but also inspires the use of economic tools to solve fairness problems in IR. We hope this tutorial provides a fresh, broad perspective on fairness in IR, highlighting open problems and future research directions.

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FairDiverse: A Comprehensive Toolkit for Fairness- and Diversity-aware Information Retrieval

Chen Xu, Zhirui Deng, Clara Rus, Xiaopeng Ye, Yuanna Liu, Jun Xu, Zhicheng Dou, Ji-Rong Wen, Maarten de Rijke

Published in SIGIR, 2025

We introduce FairDiverse, an open-source standardized toolkit to enable comprehensive, reproducible evaluation of fairness- and diversity-aware algorithms across both search and recommendation tasks in IR.

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AnnoRank: A Comprehensive Web-Based Framework for Collecting Annotations and Assessing Rankings

Clara Rus, Gabrielle Poerwawinata, Andrew Yates, Maarten de Rijke

Published in CIKM, 2024

We present AnnoRank, a web-based user interface (UI) framework designed to facilitate collecting crowdsource annotations in the context of information retrieval. AnnoRank enables the collection of explicit and implicit annotations for a specified query and a single or multiple documents, allowing for the observation of user-selected items and the assignment of relevance judgments. Furthermore, AnnoRank allows for ranking comparisons, allowing for the visualization and evaluation of a ranked list generated by different fairness interventions, along with its utility and fairness metrics.

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A Study of Pre-processing Fairness Intervention Methods for Ranking People

Clara Rus, Andrew Yates, Maarten de Rijke

Published in ECIR, 2024

Fairness interventions are hard to use in practice when ranking people due to legal constraints that limit access to sensitive information. Pre-processing fairness interventions, however, can be used in practice to create more fair training data that encourage the model to generate fair predictions without having access to sensitive information during inference. On two real-world datasets, the pre-processing methods are found to improve the diversity of rankings with respect to gender, while individual fairness is not affected. Moreover, we discuss advantages and disadvantages of using pre-processing fairness interventions in practice for ranking people.

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Counterfactual Representations for Intersectional Fair Ranking in Recruitment

Clara Rus, Maarten de Rijke and Andrew Yates

Published in Recsys@HR, 2023

Fairness interventions require access to sensitive attributes of candidates applying for a job, which might not be available due to limitations imposed by data protection laws. In this work we propose using a pre-processing technique to create counterfactual representations of the candidates that lead to a more diverse ranking with respect to intersectional groups.

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Closing the gender wage gap: Adversarial fairness in job recommendation

Clara Rus, Jeffrey Luppes, Harrie Oosterhuis, Gido H Schoenmacker

Published in Recsys@HR, 2022

The goal of this work is to help mitigate the already existing gender wage gap by supplying unbiased job recommendations based on resumes from job seekers. We employ a generative adversarial network to remove gender bias from word2vec representations.

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