数据质量评估：挑战与机遇
Data Quality Assessment: Challenges and Opportunities
AI总结：
针对数据质量维度评估研究不足的问题，本文提出涵盖数据、来源、系统、任务与人的五方面评估框架，以应对挑战并支撑AI Act相关质量维度评估。
摘要：
面向数据的应用、其用户乃至法律都要求高质量的数据。研究已将数据质量这一相当模糊的概念划分为准确性、一致性和声誉等多个维度。为实现高数据质量目标，已有许多工具和技术可用于清洗或以其他方式改进数据。然而，针对实际评估各维度数据质量的系统性研究仍基本缺失，随之缺失的是衡量任何数据清洗工作成效的能力。我们提出将五个方面作为评估数据质量的组成要素：数据、来源、系统、任务和人。利用每个方面进行数据质量评估都面临其特有挑战。我们展示了克服这些挑战如何有助于针对欧洲《AI Act》中提及的数据质量维度开展数据质量评估。本文最后提出了一个综合性数据质量评估框架。
英文摘要：
Data-oriented applications, their users, and even the law require data of high quality. Research has divided the rather vague notion of data quality into various dimensions, such as accuracy, consistency, and reputation. To achieve the goal of high data quality, many tools and techniques exist to clean and otherwise improve data. Yet, systematic research on actually assessing data quality in its dimensions is largely absent, and with it, the ability to gauge the success of any data cleaning effort. We propose five facets as ingredients to assess data quality: data, source, system, task, and human. Tapping each facet for data quality assessment poses its own challenges. We show how overcoming these challenges helps data quality assessment for those data quality dimensions mentioned in Europe's AI Act. Our work concludes with a proposal for a comprehensive data quality assessment framework.
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。
