Responsible Data Integration: Next-generation Challenges
Responsible Data Integration: Next-generation Challenges
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DOI：
10.1145/3514221.3522567
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
F. Nargesian;Abolfazl Asudeh;H. V. Jagadish
中科院分区：
文献类型：
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作者：
F. Nargesian;Abolfazl Asudeh;H. V. Jagadish
数据集成一直是数据管理界广泛研究的问题，是ML管道数据前处理的核心任务。当综合数据用于分析和模型培训时，负责任的数据科学需要解决对数据质量和偏差的担忧。我们提供了关于数据集成和责任的教程，重点介绍了在负责任的数据集成方面的现有努力以及研究机会和挑战。在本教程中，我们鼓励社区使用责任措施审核数据集成任务，并开发优化负责任数据科学需求的集成技术。我们将重点放在三个关键方面：(1)评估和审计数据集成任务的质量和偏差的要求；(2)引起人们对满足这些要求的数据责任措施和方法的关注的数据集成任务；以及(3)有助于实现数据责任的数据集成中的技术、任务和公开问题。
Data integration has been extensively studied by the data management community and is a core task in the data pre-processing step of ML pipelines. When the integrated data is used for analysis and model training, responsible data science requires addressing concerns about data quality and bias. We present a tutorial on data integration and responsibility, highlighting the existing efforts in responsible data integration along with research opportunities and challenges. In this tutorial, we encourage the community to audit data integration tasks with responsibility measures and develop integration techniques that optimize the requirements of responsible data science. We focus on three critical aspects: (1) the requirements to be considered for evaluating and auditing data integration tasks for quality and bias; (2) the data integration tasks that elicit attention to data responsibility measures and methods to satisfy these requirements; and, (3) techniques, tasks, and open problems in data integration that help achieve data responsibility.