Revisiting the fragility of influence functions

• Published in: Neural networks 2023-05, Vol.162, p.581-588
• Main Authors: Epifano, Jacob R., Ramachandran, Ravi P., Masino, Aaron J., Rasool, Ghulam
• Format: Article
• Language: English
• Subjects: Bayesian neural networks; Deep learning; Explainable AI; Influence functions; Machine learning; Supervised learning
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2023.03.029

In the last few years, many works have tried to explain the predictions of deep learning models. Few methods, however, have been proposed to verify the accuracy or faithfulness of these explanations. Recently, influence functions, which is a method that approximates the effect that leave-one-out training has on the loss function, has been shown to be fragile. The proposed reason for their fragility remains unclear. Although previous work suggests the use of regularization to increase robustness, this does not hold in all cases. In this work, we seek to investigate the experiments performed in the prior work in an effort to understand the underlying mechanisms of influence function fragility. First, we verify influence functions using procedures from the literature under conditions where the convexity assumptions of influence functions are met. Then, we relax these assumptions and study the effects of non-convexity by using deeper models and more complex datasets. Here, we analyze the key metrics and procedures that are used to validate influence functions. Our results indicate that the validation procedures may cause the observed fragility. •Influence functions do not appear to be as fragile as previously thought.•Bayesian neural nets enhance influence function explanations.•Large hessian eigenvalues do not correlate with influence function performance.•The retraining from optimal procedure may lead to erroneous results.•Spearman correlation is not an effective metric to evaluate influence functions.