Measuring Data

Margaret Mitchell; Alexandra Sasha Luccioni; Nathan Lambert; Marissa Gerchick; Angelina McMillan-Major; Ezinwanne Ozoani; Nazneen Rajani; Tristan Thrush; Yacine Jernite; Douwe Kiela

arXiv · 2212.05129

Measuring Data

Margaret Mitchell·Alexandra Sasha Luccioni·Nathan Lambert·Marissa Gerchick·Angelina McMillan-Major·Ezinwanne Ozoani·Nazneen Rajani·Tristan Thrush·Yacine Jernite·Douwe Kiela

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Abstract

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.

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Margaret Mitchell, Alexandra Sasha Luccioni, Nathan Lambert, Marissa Gerchick, Angelina McMillan-Major, Ezinwanne Ozoani, Nazneen Rajani, Tristan Thrush, Yacine Jernite, Douwe Kiela. 2022-12-09. Measuring Data. https://arxiv.org/abs/2212.05129

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