When Underrepresentation Matters: A Framework for Anticipating Fairness Risks

Diletta Longhin, Marina Ceccon, Magali Legast, Alessandro Fabris, Toon Calders, Gian Antonio Susto
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:190-195, 2026.

Abstract

Underrepresentation of minority groups in training data is widely regarded as a primary driver of unfair model behavior, motivating a broad range of data rebalancing interventions. However, recent work has begun to suggest that whether demographic imbalance actually translates into performance disparities may depend on a variety of dataset properties, which have so far been studied in isolation. In this work, we sketch a methodological pipeline to systematically study the factors that govern the impact of underrepresentation on fairness, with the goal of formulating a unified, empirically testable hypothesis that can be evaluated prior to model training. We additionally outline lightweight strategies for assessing these factors without the need to train a full model, making the approach practical for real-world data preparation workflows.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-longhin26a, title = {When Underrepresentation Matters: A Framework for Anticipating Fairness Risks}, author = {Longhin, Diletta and Ceccon, Marina and Legast, Magali and Fabris, Alessandro and Calders, Toon and Susto, Gian Antonio}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {190--195}, 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/longhin26a/longhin26a.pdf}, url = {https://proceedings.mlr.press/v350/longhin26a.html}, abstract = {Underrepresentation of minority groups in training data is widely regarded as a primary driver of unfair model behavior, motivating a broad range of data rebalancing interventions. However, recent work has begun to suggest that whether demographic imbalance actually translates into performance disparities may depend on a variety of dataset properties, which have so far been studied in isolation. In this work, we sketch a methodological pipeline to systematically study the factors that govern the impact of underrepresentation on fairness, with the goal of formulating a unified, empirically testable hypothesis that can be evaluated prior to model training. We additionally outline lightweight strategies for assessing these factors without the need to train a full model, making the approach practical for real-world data preparation workflows.} }
Endnote
%0 Conference Paper %T When Underrepresentation Matters: A Framework for Anticipating Fairness Risks %A Diletta Longhin %A Marina Ceccon %A Magali Legast %A Alessandro Fabris %A Toon Calders %A Gian Antonio Susto %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-longhin26a %I PMLR %P 190--195 %U https://proceedings.mlr.press/v350/longhin26a.html %V 350 %X Underrepresentation of minority groups in training data is widely regarded as a primary driver of unfair model behavior, motivating a broad range of data rebalancing interventions. However, recent work has begun to suggest that whether demographic imbalance actually translates into performance disparities may depend on a variety of dataset properties, which have so far been studied in isolation. In this work, we sketch a methodological pipeline to systematically study the factors that govern the impact of underrepresentation on fairness, with the goal of formulating a unified, empirically testable hypothesis that can be evaluated prior to model training. We additionally outline lightweight strategies for assessing these factors without the need to train a full model, making the approach practical for real-world data preparation workflows.
APA
Longhin, D., Ceccon, M., Legast, M., Fabris, A., Calders, T. & Susto, G.A.. (2026). When Underrepresentation Matters: A Framework for Anticipating Fairness Risks. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:190-195 Available from https://proceedings.mlr.press/v350/longhin26a.html.

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