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 the set of resultant models (and associated test-time predictions) would be if we could somehow access all hypothetical, unbiased versions of the dataset. We discuss how to use this framework to encapsulate various sources of uncertainty in datasets' factualness, including systemic social bias, data collection practices, and noisy labels or features. We show how to exactly analyze the impacts of dataset multiplicity for a specific model architecture and type of uncertainty: linear models with label errors. Our empirical analysis shows that real-world datasets, under reasonable assumptions, contain many test samples whose predictions are affected by dataset multiplicity. Furthermore, the choice of domain-specific dataset multiplicity definition determines what samples are affected, and whether different demographic groups are disparately impacted. Finally, we discuss implications of dataset multiplicity for machine learning practice and research, including considerations for when model outcomes should not be trusted.

翻译：我们提出数据集多重性概念，用于研究训练数据中的不准确性、不确定性和社会偏见如何影响测试时预测。数据集多重性框架提出反事实问题：若我们能够获取数据集所有假设的无偏版本，那么最终得到的模型集合（及其对应的测试时预测）将是什么样。我们讨论了如何利用该框架涵盖数据集真实性中各种不确定性来源，包括系统性社会偏见、数据收集实践以及噪声标签或特征。我们展示了如何精确分析特定模型架构和不确定性类型——带标签误差的线性模型——的数据集多重性影响。实证分析表明，在合理假设下，现实世界数据集包含许多预测结果受数据集多重性影响的测试样本。此外，特定领域的数据集多重性定义的选择决定了哪些样本受到影响，以及不同人口群体是否受到差异影响。最后，我们讨论了数据集多重性对机器学习实践与研究的影响，包括何时模型输出不应被信任的考量。