我们不应需要做的侦探工作：在机器学习系统中与监管对齐的数据质量实践挑战
"Detective Work We Shouldn't Have to Do": Practitioner Challenges in Regulatory-Aligned Data Quality in Machine Learning Systems
AI总结：
本文探讨了在受监管环境中机器学习系统中与监管对齐的数据质量实践挑战，揭示了法律与工程流程之间的差距以及从业者对合规工具和治理结构的需求。
摘要：
确保机器学习（ML）系统中的数据质量已变得更加复杂，因为监管要求不断扩大。在欧洲联盟（EU）中，诸如通用数据保护条例（GDPR）和人工智能法案（AI Act）等框架明确规定了与技术实践密切相关的数据质量要求，同时也扩展到与问责、风险管理以及人权保护相关的法律义务。本文通过与在受监管环境中使用机器学习系统的欧盟数据从业者进行定性访谈研究，探讨从业者如何解读与监管对齐的数据质量，他们遇到的挑战以及他们认为必要的支持。我们的发现揭示了法律原则与工程工作流程之间的持续差距，数据管道的碎片化，现有工具的局限性，技术与法律团队之间责任界限的不明确，以及倾向于反应性、审计驱动的质量实践倾向。我们还识别出从业者对合规意识工具、更清晰的治理结构以及向前瞻性数据治理转变的文化需求。
英文摘要：
Ensuring data quality in machine learning (ML) systems has become increasingly complex as regulatory requirements expand. In the European Union (EU), frameworks such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (AI Act) articulate data quality requirements that closely parallel technical concerns in ML practice, while also extending to legal obligations related to accountability, risk management, and human rights protection. This paper presents a qualitative interview study with EU-based data practitioners working on ML systems in regulated contexts. Through semi-structured interviews, we investigate how practitioners interpret regulatory-aligned data quality, the challenges they encounter, and the supports they identify as necessary. Our findings reveal persistent gaps between legal principles and engineering workflows, fragmentation across data pipelines, limitations of existing tools, unclear responsibility boundaries between technical and legal teams, and a tendency toward reactive, audit-driven quality practices. We also identify practitioners' needs for compliance-aware tooling, clearer governance structures, and cultural shifts toward proactive data governance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
