Data-centric AI is at the center of a fundamental shift in software
engineering where machine learning becomes the new software, powered by big
data and computing infrastructure. Here software engineering needs to be
re-thought where data becomes a first-class citizen on par with code. One
striking observation is that a significant portion of the machine learning
process is spent on data preparation. Without good data, even the best machine
learning algorithms cannot perform well. As a result, data-centric AI practices
are now becoming mainstream. Unfortunately, many datasets in the real world are
small, dirty, biased, and even poisoned. In this survey, we study the research
landscape for data collection and data quality primarily for deep learning
applications. Data collection is important because there is lesser need for
feature engineering for recent deep learning approaches, but instead more need
for large amounts of data. For data quality, we study data validation,
cleaning, and integration techniques. Even if the data cannot be fully cleaned,
we can still cope with imperfect data during model training using robust model
training techniques. In addition, while bias and fairness have been less
studied in traditional data management research, these issues become essential
topics in modern machine learning applications. We thus study fairness measures
and unfairness mitigation techniques that can be applied before, during, or
after model training. We believe that the data management community is well
poised to solve these problems