Individual Fairness in Algorithmic Hiring

Stavros Davidopoulos, Panagiotis Symeonidis, Dimitris Sacharidis
Proceedings of Fourth European Workshop on Algorithmic Fairness, PMLR 294:505-510, 2025.

Abstract

In this paper, we study individual fairness in job recommendations, and make two concrete contributions. To the best of our knowledge, we are the first to introduce the concept of $\varepsilon$-individual fairness to job recommendations; the smaller the value of $\varepsilon$, the stronger the guarantee of individual fairness is. Compared to existing definitions of individual fairness, e.g., for classification tasks, the output of a recommender is a ranked list of items, here jobs. Therefore, the novel aspect is that we propose the use of Kendall’s $\tau$ distance as a measure of similarity between recommendation lists. To ensure individual fairness, we introduce a novel post-processing approach. Initially, we cluster individuals with similar non-protected attributes. For each cluster, we construct a Kemeny-optimal aggregate recommendation list, which will serve as a template to generate individual recommendations. As a result, similar individuals will receive similar recommendations. Our experiments show that our method can effectively control the individual fairness guarantee, i.e., the value of $\varepsilon$.

Cite this Paper

BibTeX
@InProceedings{pmlr-v294-davidopoulos25a, title = {Individual Fairness in Algorithmic Hiring}, author = {Davidopoulos, Stavros and Symeonidis, Panagiotis and Sacharidis, Dimitris}, booktitle = {Proceedings of Fourth European Workshop on Algorithmic Fairness}, pages = {505--510}, year = {2025}, editor = {Weerts, Hilde and Pechenizkiy, Mykola and Allhutter, Doris and Corrêa, Ana Maria and Grote, Thomas and Liem, Cynthia}, volume = {294}, series = {Proceedings of Machine Learning Research}, month = {30 Jun--02 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v294/main/assets/davidopoulos25a/davidopoulos25a.pdf}, url = {https://proceedings.mlr.press/v294/davidopoulos25a.html}, abstract = {In this paper, we study individual fairness in job recommendations, and make two concrete contributions. To the best of our knowledge, we are the first to introduce the concept of $\varepsilon$-individual fairness to job recommendations; the smaller the value of $\varepsilon$, the stronger the guarantee of individual fairness is. Compared to existing definitions of individual fairness, e.g., for classification tasks, the output of a recommender is a ranked list of items, here jobs. Therefore, the novel aspect is that we propose the use of Kendall’s $\tau$ distance as a measure of similarity between recommendation lists. To ensure individual fairness, we introduce a novel post-processing approach. Initially, we cluster individuals with similar non-protected attributes. For each cluster, we construct a Kemeny-optimal aggregate recommendation list, which will serve as a template to generate individual recommendations. As a result, similar individuals will receive similar recommendations. Our experiments show that our method can effectively control the individual fairness guarantee, i.e., the value of $\varepsilon$.} }
Endnote
%0 Conference Paper %T Individual Fairness in Algorithmic Hiring %A Stavros Davidopoulos %A Panagiotis Symeonidis %A Dimitris Sacharidis %B Proceedings of Fourth European Workshop on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2025 %E Hilde Weerts %E Mykola Pechenizkiy %E Doris Allhutter %E Ana Maria Corrêa %E Thomas Grote %E Cynthia Liem %F pmlr-v294-davidopoulos25a %I PMLR %P 505--510 %U https://proceedings.mlr.press/v294/davidopoulos25a.html %V 294 %X In this paper, we study individual fairness in job recommendations, and make two concrete contributions. To the best of our knowledge, we are the first to introduce the concept of $\varepsilon$-individual fairness to job recommendations; the smaller the value of $\varepsilon$, the stronger the guarantee of individual fairness is. Compared to existing definitions of individual fairness, e.g., for classification tasks, the output of a recommender is a ranked list of items, here jobs. Therefore, the novel aspect is that we propose the use of Kendall’s $\tau$ distance as a measure of similarity between recommendation lists. To ensure individual fairness, we introduce a novel post-processing approach. Initially, we cluster individuals with similar non-protected attributes. For each cluster, we construct a Kemeny-optimal aggregate recommendation list, which will serve as a template to generate individual recommendations. As a result, similar individuals will receive similar recommendations. Our experiments show that our method can effectively control the individual fairness guarantee, i.e., the value of $\varepsilon$.
APA
Davidopoulos, S., Symeonidis, P. & Sacharidis, D.. (2025). Individual Fairness in Algorithmic Hiring. Proceedings of Fourth European Workshop on Algorithmic Fairness, in Proceedings of Machine Learning Research 294:505-510 Available from https://proceedings.mlr.press/v294/davidopoulos25a.html.

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