XAI in Automated Fact-Checking? The Benefits Are Modest and There's No One-Explanation-Fits-All

TL;DR

This study evaluates the impact of explainable AI in automated fact-checking, finding modest benefits in user agreement and reliance, with significant variability in user preferences and limited influence on sharing intent.

Contribution

It provides empirical insights into how XAI affects user perceptions and behaviors in automated fact-checking, highlighting its limited effectiveness and user polarization.

Findings

XAI has limited effect on user agreement with fact-checker predictions.

XAI influences users to form more uniform judgments.

User preferences towards XAI are highly polarized.

Abstract

The massive volume of online information along with the issue of misinformation has spurred active research in the automation of fact-checking. Like fact-checking by human experts, it is not enough for an automated fact-checker to just be accurate, but also be able to inform and convince the user of the validity of its predictions. This becomes viable with explainable artificial intelligence (XAI). In this work, we conduct a study of XAI fact-checkers involving 180 participants to determine how users' actions towards news and their attitudes towards explanations are affected by the XAI. Our results suggest that XAI has limited effects on users' agreement with the veracity prediction of the automated fact-checker and on their intent to share news. However, XAI nudges users towards forming uniform judgments of news veracity, thereby signaling their reliance on the explanations. We also found polarizing preferences towards XAI and raise several design considerations on them.