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publications
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.
</article> </div>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.
</article> </div>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.
</article> </div>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.
</article> </div>talks
My master thesis research has been featured in a news article!
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Participating at the 21st edition of the Dutch-Belgian Information Retrieval Workshop (DIR)
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Organizing and participating at the European Summer School in Information Retrieval (ESSIR)
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Invited Speaker at Annie Romein-Verschoor lecture.
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Giving a presentation on software design principles for fair recruitment systems
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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