Measuring Information in Text Explanations

TL;DR

This paper introduces an information-theoretic framework to evaluate text explanations in explainable AI, unifying different methods by quantifying relevance and informativeness through communication channels.

Contribution

It proposes a novel information-theoretic approach to assess text explanations, providing standardized metrics and insights into explanation mechanisms.

Findings

NLEs balance input and target information transmission.

Rationales do not show a trade-off between information types.

Information scores reveal underlying explanation differences.

Abstract

Text-based explanation is a particularly promising approach in explainable AI, but the evaluation of text explanations is method-dependent. We argue that placing the explanations on an information-theoretic framework could unify the evaluations of two popular text explanation methods: rationale and natural language explanations (NLE). This framework considers the post-hoc text pipeline as a series of communication channels, which we refer to as ``explanation channels''. We quantify the information flow through these channels, thereby facilitating the assessment of explanation characteristics. We set up tools for quantifying two information scores: relevance and informativeness. We illustrate what our proposed information scores measure by comparing them against some traditional evaluation metrics. Our information-theoretic scores reveal some unique observations about the underlying mechanisms of two representative text explanations. For example, the NLEs trade-off slightly between transmitting the input-related information and the target-related information, whereas the rationales do not exhibit such a trade-off mechanism. Our work contributes to the ongoing efforts in establishing rigorous and standardized evaluation criteria in the rapidly evolving field of explainable AI.