INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations

Abstract

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decisionmaking, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a single explanation, which fails to account for the diversity of human thoughts and experiences in language. This paper thus addresses this gap, by proposing a generative XAI framework, INTERACTION (explaIn aNd predicT thEn queRy with contextuAl CondiTional varIational autO-eNcoder). Our novel framework presents explanation in two steps: (step one) Explanation and Label Prediction; and (step two) Diverse Evidence Generation. We conduct intensive experiments with the Transformer architecture on a benchmark dataset, e-SNLI [1]. Our method achieves competitive or better performance against state-of-the-art baseline models on explanation generation (up to 4.7% gain in BLEU) and prediction (up to 4.4% gain in accuracy) in step one; it can also generate multiple diverse explanations in step two.

Exploring explainable AI in the tax domain

“…Current explainability methods lack in terms of causality: the presentation of model's relevant modules and input data, which does not necessarily end in user's satisfaction and understanding in the context of a given task (Holzinger et al 2019). With the use of generative AI the new possibilities of generating explanations are presented and they yield promising results that have capacity to make XAI easy to understand by laypersons (Yu et al 2022).…”

Revealing More Details: Image Super-Resolution for Real-World Applications

“…Although the emerging discipline of explainable AI (XAI) has been comprehensively studied for discriminative models [49,64], much less attention has been given to generative models, in general [70]. The few existing works in the literature mostly deal with natural language [85], and software code inference [71], but none consider the problem of image restoration, such as SR.…”