Uncertainty Quantification for Local Model Explanations Without Model Access
This work addresses the need for uncertainty quantification in model explanations under constraints like privacy, security, or limited access, which is incremental as it builds on existing explanation methods by adding uncertainty estimation without requiring model access.
The paper tackles the problem of generating post-hoc explanations with uncertainty intervals for machine learning models when only a static sample of inputs and outputs is available, without direct model access. It presents a model-agnostic bootstrapping algorithm that shows favorable trade-offs in interval width and coverage probability compared to naive and Bayesian methods in simulations and real-world datasets.
We present a model-agnostic algorithm for generating post-hoc explanations and uncertainty intervals for a machine learning model when only a static sample of inputs and outputs from the model is available, rather than direct access to the model itself. This situation may arise when model evaluations are expensive; when privacy, security and bandwidth constraints are imposed; or when there is a need for real-time, on-device explanations. Our algorithm uses a bootstrapping approach to quantify the uncertainty that inevitably arises when generating explanations from a finite sample of model queries. Through a simulation study, we show that the uncertainty intervals generated by our algorithm exhibit a favorable trade-off between interval width and coverage probability compared to the naive confidence intervals from classical regression analysis as well as current Bayesian approaches for quantifying explanation uncertainty. We further demonstrate the capabilities of our method by applying it to black-box models, including a deep neural network, trained on three real-world datasets.