Title: Understanding Black-box Predictions via Influence Functions,
Abstract: How can we explain the predictions of a black-box model? In this paper, we
use influence functions -- a classic technique from robust statistics -- to
trace a model's prediction through the learning algorithm and back to its
training data, thereby identifying training points most responsible for a given
prediction. To scale up influence functions to modern machine learning
settings, we develop a simple, efficient implementation that requires only
oracle access to gradients and Hessian-vector products. We show that even on
non-convex and non-differentiable models where the theory breaks down,
approximations to influence functions can still provide valuable information.
On linear models and convolutional neural networks, we demonstrate that
influence functions are useful for multiple purposes: understanding model
behavior, debugging models, detecting dataset errors, and even creating
visually-indistinguishable training-set attacks.