机器学习从业者在欧盟监管要求下的数据质量观点：一项欧洲在线调查
Machine Learning Practitioners' Views on Data Quality in Light of EU Regulatory Requirements: A European Online Survey
AI总结：
本文通过欧洲在线调查，探讨了欧盟监管下机器学习从业者对数据质量的认知与实践，揭示了当前数据质量实践与监管要求之间的差距，并提出改进数据质量工具和协作的建议。
摘要：
理解数据质量如何与机器学习（ML）系统中的监管要求对齐，对于在不断演变的欧盟监管环境中导航的从业者来说是一个关键挑战。为此，我们首先提出一个将已建立的数据质量维度与特定欧盟监管要求对齐的实用框架。其次，我们进行了涵盖超过180名欧盟数据从业者的全面在线调查，调查他们在确保符合监管要求的ML系统中数据质量时的方法、关键挑战和未满足需求。我们的发现突显了当前实践与监管期望之间的关键差距，强调了从业者对更集成的数据质量工具和更紧密的技术与法律从业者协作的需要。这些见解为弥合技术专长与监管合规性之间的差距提供了建议，最终促进负责任和可信的ML部署。
英文摘要：
Understanding how data quality aligns with regulatory requirements in machine learning (ML) systems presents a critical challenge for practitioners navigating the evolving EU regulatory landscape. To address this, we first propose a practical framework aligning established data quality dimensions with specific EU regulatory requirements. Second, we conducted a comprehensive online survey with over 180 EU-based data practitioners, investigating their approaches, key challenges, and unmet needs when ensuring data quality in ML systems that align with regulatory requirements. Our findings highlight crucial gaps between current practices and regulatory expectations, underscoring practitioners' need for more integrated data quality tools and better collaboration between technical and legal practitioners. These insights inform recommendations for bridging technical expertise and regulatory compliance, ultimately fostering responsible and trustworthy ML deployments.
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。
