It is worth noting that influence functions are guaranteed to be accurate only when the model is strictly convex (i.e., its Hessian is positive definite and thus invertible) and is trained to convergence.
we are interested in the use of influence functions (Koh and Liang, 2017), which are in a sense inherently 'faithful' in that they reveal the training examples most responsible for particular predictions.