Underrepresentation, label bias, and proxies: Towards Data Bias Profiles for the EU AI act and beyond

• We study three fundamental data bias factors and their importance for fairness in AI. • We show that group-dependent label noise is more critical than under-representation. • We study bias detection mechanisms aiding data documentation for the EU AI Act. • We propose Data Bias Profiles, a preliminary construct to characterize datasets by their biases. • We distill our findings in guidelines for fairness researchers and practitioners. Undesirable biases encoded in the data are key drivers of algorithmic discrimination. Their importance is widely recognized in the algorithmic fairness literature, as well as legislation and standards on anti-discrimination in AI. Despite this recognition, data biases remain understudied, hindering the development of computational best practices for their detection and mitigation. In this work, we present three common data biases and study their individual and joint effect on algorithmic discrimination across a variety of datasets, models, and fairness measures. We find that underrepresentation of vulnerable populations in training sets is less conducive to discrimination than conventionally affirmed, while combinations of proxies and label bias can be far more critical. Consequently, we develop dedicated mechanisms to detect specific types of bias, and combine them into a preliminary construct we refer to as the Data Bias Profile (DBP) . This initial formulation serves as a proof of concept for how different bias signals can be systematically documented. Through a case study with popular fairness datasets, we demonstrate the effectiveness of the DBP in predicting the risk of discriminatory outcomes and the utility of fairness-enhancing interventions. Overall, this article bridges algorithmic fairness research and anti-discrimination policy through a data-centric lens.