at Google ScholarTitle data
Deck, Luca ; Zipperling, Domenique ; Jessat, Leo ; Kühl, Niklas:
Data-Centric Algorithmic Fairness : A Systematic Review of Data-Level Interventions for Algorithmic Fairness.
2026
Event: European Conference on Algorithmic Fairness (ECAF)
, 2-4 September 2026
, Ghent, Belgium.
(Conference item: Conference
,
Speech
)
Abstract in another language
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.