Local data attribution (or influence estimation) techniques aim at estimating
the impact that individual data points seen during training have on particular
predictions of an already trained Machine Learning model during test time.
Previous methods either do not perform well consistently across different
evaluation criteria from literature, are characterized by a high computational
demand, or suffer from both. In this work we present DualView, a novel method
for post-hoc data attribution based on surrogate modelling, demonstrating both
high computational efficiency, as well as good evaluation results. With a focus
on neural networks, we evaluate our proposed technique using suitable
quantitative evaluation strategies from the literature against related
principal local data attribution methods. We find that DualView requires
considerably lower computational resources than other methods, while
demonstrating comparable performance to competing approaches across evaluation
metrics. Futhermore, our proposed method produces sparse explanations, where
sparseness can be tuned via a hyperparameter. Finally, we showcase that with
DualView, we can now render explanations from local data attributions
compatible with established local feature attribution methods: For each
prediction on (test) data points explained in terms of impactful samples from
the training set, we are able to compute and visualize how the prediction on
(test) sample relates to each influential training sample in terms of features
recognized and by the model. We provide an Open Source implementation of
DualView online, together with implementations for all other local data
attribution methods we compare against, as well as the metrics reported here,
for full reproducibility.

本研究介绍了一种基于替代建模的后续数据归因方法 DualView，它在计算效率和评估结果方面表现良好。通过使用适合的定量评估策略和相关的局部数据归因方法，我们发现 DualView 方法在需要较低计算资源的同时，表现不亚于其他方法。此外，该方法产生稀疏的解释，可以通过超参数进行调整。最后，我们展示了 DualView 可以将局部数据归因的解释与已有的局部特征归因方法兼容，并提供了在线的开源实现，以及与其他局部数据归因方法和此处报告的度量标准的实现，以便进行完全可复现性研究。