Fairness for Dating Apps: Mitigating Discrimination by Preventing Popularity Bias

Tim De Jonge, Djoerd Hiemstra
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:201-216, 2026.

Abstract

Dating apps are important intermediaries in people finding romance, yet dating algorithms are likely to discriminate on basis of ethnicity. Dating apps cannot sufficiently measure or mitigate discrimination, as the GDPR prevents dating apps from accessing or processing ethnicity. In this work, we show that popularity bias can be a driver for discrimination, and that mitigating popularity bias can result in reduced algorithmic discrimination in content-based recommendation. Interventions on popularity bias appear more effective in content-based recommendation, with an appropriately chosen definition of popularity bias, and targeting indirect discrimination, rather than direct discrimination.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-de-jonge26a, title = {Fairness for Dating Apps: Mitigating Discrimination by Preventing Popularity Bias}, author = {De Jonge, Tim and Hiemstra, Djoerd}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {201--216}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/de-jonge26a/de-jonge26a.pdf}, url = {https://proceedings.mlr.press/v350/de-jonge26a.html}, abstract = {Dating apps are important intermediaries in people finding romance, yet dating algorithms are likely to discriminate on basis of ethnicity. Dating apps cannot sufficiently measure or mitigate discrimination, as the GDPR prevents dating apps from accessing or processing ethnicity. In this work, we show that popularity bias can be a driver for discrimination, and that mitigating popularity bias can result in reduced algorithmic discrimination in content-based recommendation. Interventions on popularity bias appear more effective in content-based recommendation, with an appropriately chosen definition of popularity bias, and targeting indirect discrimination, rather than direct discrimination.} }
Endnote
%0 Conference Paper %T Fairness for Dating Apps: Mitigating Discrimination by Preventing Popularity Bias %A Tim De Jonge %A Djoerd Hiemstra %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-de-jonge26a %I PMLR %P 201--216 %U https://proceedings.mlr.press/v350/de-jonge26a.html %V 350 %X Dating apps are important intermediaries in people finding romance, yet dating algorithms are likely to discriminate on basis of ethnicity. Dating apps cannot sufficiently measure or mitigate discrimination, as the GDPR prevents dating apps from accessing or processing ethnicity. In this work, we show that popularity bias can be a driver for discrimination, and that mitigating popularity bias can result in reduced algorithmic discrimination in content-based recommendation. Interventions on popularity bias appear more effective in content-based recommendation, with an appropriately chosen definition of popularity bias, and targeting indirect discrimination, rather than direct discrimination.
APA
De Jonge, T. & Hiemstra, D.. (2026). Fairness for Dating Apps: Mitigating Discrimination by Preventing Popularity Bias. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:201-216 Available from https://proceedings.mlr.press/v350/de-jonge26a.html.

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