Interactive Coreset Selection for Tabular Data: Fairness-Aware, Explainable, and User-Guided

We present a human-centric extension of CoreTab, a novel coreset algorithm for tabular data recently accepted to VLDB 2025. A core-set is a compact, representative subset of a dataset that approximates full-data training performance while greatly reducing computational cost. While CoreTab already achieves state-of-the-art accuracy and efficiency, this demonstration focuses on its interactive components that let users audit, adjust, and guide data selection—addressing real-world concerns like fairness and representativeness. CoreTab is the first coreset method with built-in explainability, offering a decision-tree-based view of which data regions were included or excluded. It also introduces the first human-in-the-loop interface for coreset refinement. We highlight design insights and open challenges for building transparent and responsible data sampling workflows.