Most popular ML/AI datasets are poorly documented, especially regarding collection, processing, and maintenance, according to a manual audit of 100 datasets across four repositories.
A Methodology for Creating AI FactSheets
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abstract
As AI models and services are used in a growing number of highstakes areas, a consensus is forming around the need for a clearer record of how these models and services are developed to increase trust. Several proposals for higher quality and more consistent AI documentation have emerged to address ethical and legal concerns and general social impacts of such systems. However, there is little published work on how to create this documentation. This is the first work to describe a methodology for creating the form of AI documentation we call FactSheets. We have used this methodology to create useful FactSheets for nearly two dozen models. This paper describes this methodology and shares the insights we have gathered. Within each step of the methodology, we describe the issues to consider and the questions to explore with the relevant people in an organization who will be creating and consuming the AI facts in a FactSheet. This methodology will accelerate the broader adoption of transparent AI documentation.
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cs.DL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation
Most popular ML/AI datasets are poorly documented, especially regarding collection, processing, and maintenance, according to a manual audit of 100 datasets across four repositories.