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.

介绍了一种名为 “数据集多样性” 的框架，用于研究训练数据集中的不准确性、不确定性和社会偏见如何影响测试时间的预测，以及该框架在机器学习实践和研究中的考虑。

数据集多样性问题：不可靠数据如何影响预测

The Dataset Multiplicity Problem: How Unreliable Data Impacts  Predictions

Influence functions approximate the effect of training samples in test-time
predictions and have a wide variety of applications in machine learning
interpretability and uncertainty estimation. A commonly-used (first-order)
influence function can be implemented efficiently as a post-hoc method
requiring access only to the gradients and Hessian of the model. For linear
models, influence functions are well-defined due to the convexity of the
underlying loss function and are generally accurate even across difficult
settings where model changes are fairly large such as estimating group
influences. Influence functions, however, are not well-understood in the
context of deep learning with non-convex loss functions. In this paper, we
provide a comprehensive and large-scale empirical study of successes and
failures of influence functions in neural network models trained on datasets
such as Iris, MNIST, CIFAR-10 and ImageNet. Through our extensive experiments,
we show that the network architecture, its depth and width, as well as the
extent of model parameterization and regularization techniques have strong
effects in the accuracy of influence functions. In particular, we find that (i)
influence estimates are fairly accurate for shallow networks, while for deeper
networks the estimates are often erroneous; (ii) for certain network
architectures and datasets, training with weight-decay regularization is
important to get high-quality influence estimates; and (iii) the accuracy of
influence estimates can vary significantly depending on the examined test
points. These results suggest that in general influence functions in deep
learning are fragile and call for developing improved influence estimation
methods to mitigate these issues in non-convex setups.

本文举行了一项大规模实证研究，详细探究了影响函数在神经网络模型中的成功和失败，在浅层网络中影响估计值相对准确，在深层网络中影响估计值通常是错误的，特定的神经网络结构和数据集，训练时使用重量减退正规化很重要以获得高质量的影响估计。