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We introduce dataset multiplicity, a way to study how inaccuracies,uncertainty, and social bias in training datasets impact test-time predictions.The dataset multiplicity framework asks a counterfactual question of what theset of resultant models (and associated test-time predictions) would be if wecould somehow access all hypothetical, unbiased versions of the dataset. Wediscuss how to use this framework to encapsulate various sources of uncertaintyin datasets' factualness, including systemic social bias, data collectionpractices, and noisy labels or features. We show how to exactly analyze theimpacts of dataset multiplicity for a specific model architecture and type ofuncertainty: linear models with label errors. Our empirical analysis shows thatreal-world datasets, under reasonable assumptions, contain many test sampleswhose predictions are affected by dataset multiplicity. Furthermore, the choiceof domain-specific dataset multiplicity definition determines what samples areaffected, and whether different demographic groups are disparately impacted.Finally, we discuss implications of dataset multiplicity for machine learningpractice and research, including considerations for when model outcomes shouldnot be trusted.

The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions