F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering

Abstract

Explainable question answering systems predict an answer together with an explanation showing why the answer has been selected. The goal is to enable users to assess the correctness of the system and understand its reasoning process. However, we show that current models and evaluation settings have shortcomings regarding the coupling of answer and explanation which might cause serious issues in user experience. As a remedy, we propose a hierarchical model and a new regularization term to strengthen the answer-explanation coupling as well as two evaluation scores to quantify the coupling. We conduct experiments on the HOTPOTQA benchmark data set and perform a user study. The user study shows that our models increase the ability of the users to judge the correctness of the system and that scores like F 1 are not enough to estimate the usefulness of a model in a practical setting with human users. Our scores are better aligned with user experience, making them promising candidates for model selection.

The Role of Human Knowledge in Explainable AI

“…On such a topic, while a variety of evaluation methods and approaches have been proposed [63], it is still argued that the best way to assess the interpretability of black-box models is through user experiments and user-centred evaluations as there is no guarantee for the correctness of automated metrics in evaluating explainability [64] and high explainability metric scores do not necessarily reflect high human interpretability in real-world scenarios [64,65]. The same is true for well-known metrics (e.g., F1-score) [66]. Supporting such claims, Fel et al [65] conducted experiments to evaluate the capability of human participants to leverage representative attribution methods to learn to predict the decision of various image classifiers.…”

“…The same approach is applicable to the evaluation of the interpretability of black-box models, i.e., directly understanding the intrinsic explainability of a model [67]. Such evaluations are usually achieved through user questionnaires [66,[68][69][70] whose questions vary depending on the nature of the experiment, model, etc. On the other hand, comparing the interpretability of different explainability methods to choose the best suited one requires the design and implementation of ad hoc human-in-the-loop approaches.…”