🤖 AI Summary
研究通过SMT求解方法验证高风险ML系统的数据集质量，评估了数据属性类型、规范风格和编码策略对验证性能的影响。
📝 Abstract
The EU AI Act mandates that datasets for high-risk machine learning (ML) systems meet strict quality criteria such as soundness and bias mitigation. While Satisfiability Modulo Theory (SMT) solving offers a formal approach to verifying these properties, its scalability in realistic ML settings remains unexplored. To bridge this gap, this work presents the first large-scale empirical study of SMT-based dataset verification on two real-world ML datasets. We systematically evaluate how solver performance is shaped by three key dimensions: the type of data-quality property, the specification style, and the dataset encoding strategy. Our findings demonstrate that SMT-based verification is feasible for practical scenarios, but each dimension shapes it: the property type sets the tractability limit, the specification style drives scalability (exceeding 2000x for aggregate properties), and the encoding strategy has a systematic effect, with extracted feature columns performing best.