Use-Case-Grounded Simulations for Explanation Evaluation
This addresses the problem of inefficient user study design for researchers in explainable AI, offering a screening tool to reduce costs, though it is incremental as it builds on existing evaluation methods.
The paper tackles the challenge of costly human evaluations for explanation methods by introducing Use-Case-Grounded Simulated Evaluations (SimEvals), which use algorithmic agents to predict which explanations help humans in real-world use cases, demonstrating effectiveness across three use cases.
A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use cases. However, running user studies is challenging and costly, and consequently each study typically only evaluates a limited number of different settings, e.g., studies often only evaluate a few arbitrarily selected explanation methods. To address these challenges and aid user study design, we introduce Use-Case-Grounded Simulated Evaluations (SimEvals). SimEvals involve training algorithmic agents that take as input the information content (such as model explanations) that would be presented to each participant in a human subject study, to predict answers to the use case of interest. The algorithmic agent's test set accuracy provides a measure of the predictiveness of the information content for the downstream use case. We run a comprehensive evaluation on three real-world use cases (forward simulation, model debugging, and counterfactual reasoning) to demonstrate that Simevals can effectively identify which explanation methods will help humans for each use case. These results provide evidence that SimEvals can be used to efficiently screen an important set of user study design decisions, e.g. selecting which explanations should be presented to the user, before running a potentially costly user study.