This work examines the representation of protected attributes across tabular datasets used in algorithmic fairness research. Drawing from international human rights and anti-discrimination laws, we compile a set of protected attributes and investigate both their availability and usage in the literature. Our analysis reveals a significant underrepresentation of certain attributes in datasets that is exacerbated by a strong focus on race and sex in dataset usage. We identify a geographical bias towards the Global North, particularly North America, potentially limiting the applicability of fairness detection and mitigation strategies in less-represented regions. The study exposes critical blindspots in fairness research, highlighting the need for a more inclusive and representative approach to data collection and usage in the field. We propose a shift away from a narrow focus on a small number of datasets and advocate for initiatives aimed at sourcing more diverse and representative data.
