We identify the task of measuring data to quantitatively characterize the
composition of machine learning data and datasets. Similar to an object's
height, width, and volume, data measurements quantify different attributes of
data along common dimensions that support comparison. Several lines of research
have proposed what we refer to as measurements, with differing terminology; we
bring some of this work together, particularly in fields of computer vision and
language, and build from it to motivate measuring data as a critical component
of responsible AI development. Measuring data aids in systematically building
and analyzing machine learning (ML) data towards specific goals and gaining
better control of what modern ML systems will learn. We conclude with a
discussion of the many avenues of future work, the limitations of data
measurements, and how to leverage these measurement approaches in research and
practice.

本文旨在通过测量数据来量化机器学习数据和数据集的组成部分，以促进机器学习的系统构建和分析，使现代机器学习系统更加准确和可控，同时讨论了未来工作的许多途径、数据测量的局限性，以及如何在研究和实践中利用这些测量方法。