Documentation to facilitate communication between dataset creators and consumers

DATA PLAYS A critical role in machine learning. Every machine learning model is trained and evaluated using data, quite often in the form of static datasets. The characteristics of these datasets fundamentally influence a model's behavior: a model is unlikely to perform well in the wild if its deployment context does not match its training or evaluation datasets, or if these datasets reflect unwanted societal biases. Mismatches like this can have especially severe consequences when machine learning models are used in high-stakes domains, such as criminal justice,(1,13,24) hiring,(19) critical infrastructure,(11,21) and finance.(18) Even in other domains, mismatches may lead to loss of revenue or public relations setbacks. Of particular concern are recent examples showing that machine learning models can reproduce or amplify unwanted societal biases reflected in training datasets.(4,5,12) For these and other reasons, the World Economic Forum suggests all entities should document the provenance, creation, and use of machine learning datasets to avoid discriminatory outcomes.(25) Although data provenance has been studied

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Data plays a critical role in machine learning, with mismatched datasets potentially leading to negative model behaviors and societal biases amplification. The World Economic Forum suggests documenting the origin, creation, and use of machine learning datasets to prevent discriminatory outcomes.