Analyzing ML Data Cards

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In a new ICLR 2024 paper (https://arxiv.org/abs/2401.13822), we delve into the intricacies of machine learning data cards, shedding light on what developers document and often overlook. Our analysis, which encompasses over 7,000 data cards from Hugging Face, reveals several noteworthy findings: Many recommendations for data card content are disregarded. There's a notable emphasis on Data Descriptions, yet less attention is paid to Consideration for Using Data. We categorize the topics discussed in each section of the data cards. This research underscores the significance of comprehensive data documentation in fostering responsible AI practices. By understanding and addressing the gaps in data card content, we take a crucial step toward ensuring transparency, accountability, and ethical considerations in machine learning development.