Ensuring High-Quality Private Data for Responsible Data Science: Vision and Challenges

High-quality data is critical for effective data science. As the use of data science has grown, so too have concerns that individuals' rights to privacy will be violated. This has led to the development of data protection regulations around the globe and the use of sophisticated anonymization techniques to protect privacy. Such measures make it more challenging for the data scientist to understand the data, exacerbating issues of data quality. Responsible data science aims to develop useful insights from the data while fully embracing these considerations. We pose the high-level problem in this article, "How can a data scientist develop the needed trust that private data has high quality?" We then identify a series of challenges for various data-centric communities and outline research questions for data quality and privacy researchers, which would need to be addressed to effectively answer the problem posed in this article.