Lastly, we lay out future directions for the Dataset Nutrition Label project, including research and public policy agendas to further advance consideration of the concept. ![]() We discuss ways to move forward given the limitations identified. Researchers interested in measuring dietary intake. We also explore the limitations of the Label, including the challenges of generalizing across diverse datasets, and the risk of using "ground truth" data as a comparison dataset. General guidelines on the collection and analysis of dietary data in research studies. For those building and publishing datasets, the Label creates an expectation of explanation, which will drive better data collection practices. For data specialists, the Label will drive more robust data analysis practices, provide an efficient way to select the best dataset for their purposes, and increase the overall quality of AI models as a result of more robust training datasets and the ability to check for issues at the time of model development. To demonstrate and advance this concept, we generated and published an open source prototype with seven sample modules on the ProPublica Dollars for Docs dataset. Building a Label that can be applied across domains and data types requires that the framework itself be flexible and adaptable as such, the Label is comprised of diverse qualitative and quantitative modules generated through multiple statistical and probabilistic modelling backends, but displayed in a standardized format. The Dataset Nutrition Label (the Label) is a diagnostic framework that lowers the barrier to standardized data analysis by providing a distilled yet comprehensive overview of dataset "ingredients" before AI model development. ![]() ![]() Current methods of data analysis, particularly before model development, are costly and not standardized. Download a PDF of the paper titled The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards, by Sarah Holland and 4 other authors Download PDF Abstract:Artificial intelligence (AI) systems built on incomplete or biased data will often exhibit problematic outcomes.
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