Data-Centric Algorithmic Fairness: A Systematic Review of Data-Level Interventions for Algorithmic Fairness

Domenique Zipperling, Luca Deck, Leo Jessat, Niklas Kühl
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:113-135, 2026.

Abstract

Algorithmic fairness has historically largely focused on model constraints and post-hoc adjustments to satisfy fairness metrics. However, such interventions often impose top-down fairness constraints that remain politically and legally contested and frequently encounter resistance from practitioners, e.g., due to feared accuracy-fairness trade-offs. In this paper, we investigate how the “Data-Centric AI” paradigm, i.e., improving performance through data-level interventions, can be applied toward algorithmic fairness metrics by systematically reviewing 79 papers and mapping the identified data-level fairness interventions to the two pillars of data-centric AI: data refinement (e.g., data cleaning and feature engineering) and data extension (e.g., targeted acquisition of instances and features). Our findings reveal three patterns present in current research: First, extension strategies remain under-explored despite their potential to address systemic imbalances. Second, most studies treat fairness metrics as a secondary or hindsight consideration with limited depth of empirical evaluation, which calls for more efforts focused on specific fairness objectives along the entire lifecycle. Third, we observe a lack of consistent terminology and guidance for the effective selection of techniques for practitioners. We propose the data-centric algorithmic fairness paradigm to establish a unified language for data-level fairness interventions and highlight its potential to augment model-centric approaches for future applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-zipperling26a, title = {Data-Centric Algorithmic Fairness: A Systematic Review of Data-Level Interventions for Algorithmic Fairness}, author = {Zipperling, Domenique and Deck, Luca and Jessat, Leo and K\"uhl, Niklas}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {113--135}, 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/zipperling26a/zipperling26a.pdf}, url = {https://proceedings.mlr.press/v350/zipperling26a.html}, abstract = {Algorithmic fairness has historically largely focused on model constraints and post-hoc adjustments to satisfy fairness metrics. However, such interventions often impose top-down fairness constraints that remain politically and legally contested and frequently encounter resistance from practitioners, e.g., due to feared accuracy-fairness trade-offs. In this paper, we investigate how the “Data-Centric AI” paradigm, i.e., improving performance through data-level interventions, can be applied toward algorithmic fairness metrics by systematically reviewing 79 papers and mapping the identified data-level fairness interventions to the two pillars of data-centric AI: data refinement (e.g., data cleaning and feature engineering) and data extension (e.g., targeted acquisition of instances and features). Our findings reveal three patterns present in current research: First, extension strategies remain under-explored despite their potential to address systemic imbalances. Second, most studies treat fairness metrics as a secondary or hindsight consideration with limited depth of empirical evaluation, which calls for more efforts focused on specific fairness objectives along the entire lifecycle. Third, we observe a lack of consistent terminology and guidance for the effective selection of techniques for practitioners. We propose the data-centric algorithmic fairness paradigm to establish a unified language for data-level fairness interventions and highlight its potential to augment model-centric approaches for future applications.} }
Endnote
%0 Conference Paper %T Data-Centric Algorithmic Fairness: A Systematic Review of Data-Level Interventions for Algorithmic Fairness %A Domenique Zipperling %A Luca Deck %A Leo Jessat %A Niklas Kühl %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-zipperling26a %I PMLR %P 113--135 %U https://proceedings.mlr.press/v350/zipperling26a.html %V 350 %X Algorithmic fairness has historically largely focused on model constraints and post-hoc adjustments to satisfy fairness metrics. However, such interventions often impose top-down fairness constraints that remain politically and legally contested and frequently encounter resistance from practitioners, e.g., due to feared accuracy-fairness trade-offs. In this paper, we investigate how the “Data-Centric AI” paradigm, i.e., improving performance through data-level interventions, can be applied toward algorithmic fairness metrics by systematically reviewing 79 papers and mapping the identified data-level fairness interventions to the two pillars of data-centric AI: data refinement (e.g., data cleaning and feature engineering) and data extension (e.g., targeted acquisition of instances and features). Our findings reveal three patterns present in current research: First, extension strategies remain under-explored despite their potential to address systemic imbalances. Second, most studies treat fairness metrics as a secondary or hindsight consideration with limited depth of empirical evaluation, which calls for more efforts focused on specific fairness objectives along the entire lifecycle. Third, we observe a lack of consistent terminology and guidance for the effective selection of techniques for practitioners. We propose the data-centric algorithmic fairness paradigm to establish a unified language for data-level fairness interventions and highlight its potential to augment model-centric approaches for future applications.
APA
Zipperling, D., Deck, L., Jessat, L. & Kühl, N.. (2026). Data-Centric Algorithmic Fairness: A Systematic Review of Data-Level Interventions for Algorithmic Fairness. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:113-135 Available from https://proceedings.mlr.press/v350/zipperling26a.html.

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