Data Subcard: Evaluating Privacy, Fairness, Quality, and Protection in Tabular Data, as Part of the System Cards Framework

Abstract

Medical datasets play a crucial role in advancing healthcare research and supporting clinical decision-making. At the same time, the reliability of responsible and accountable AI systems is directly dependent on the integrity and transparency of the datasets on which they are built. The data subcard implements the System Cards framework's data assessment dimension to evaluate tabular medical datasets across four criteria: privacy, fairness, quality, and protection. It combines data-level profiling with optional model-based diagnostics, selected to fit each dataset, to assess completeness, duplication, outliers, demographic dispar-ities, re-identification risk, and compliance readiness. Applied to the UCI Heart Disease and Diabetes Readmission datasets, the method flags privacy risks, fairness imbalances, quality defects, and protection gaps that warrant review before modeling. The data subcard produces quantitative scores and visual summaries, providing a structured and interpretable mechanism for dataset accountability within the System Cards framework.

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