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Datasets have played a foundational role in the advancement of machinelearning research. They form the basis for the models we design and deploy, aswell as our primary medium for benchmarking and evaluation. Furthermore, theways in which we collect, construct and share these datasets inform the kindsof problems the field pursues and the methods explored in algorithmdevelopment. However, recent work from a breadth of perspectives has revealedthe limitations of predominant practices in dataset collection and use. In thispaper, we survey the many concerns raised about the way we collect and use datain machine learning and advocate that a more cautious and thoroughunderstanding of data is necessary to address several of the practical andethical issues of the field.

Data and its (dis)contents: A survey of dataset development and use in machine learning research