research-article

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

Authors:

Simon T. PerraultAuthors Info & Claims

OzCHI '23: Proceedings of the 35th Australian Computer-Human Interaction Conference

Pages 624 - 638

https://doi.org/10.1145/3638380.3638388

Published: 10 May 2024 Publication History

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.

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Cited By

Gruzd AMai PSoares F(2024)To Share or Not to Share: Randomized Controlled Study of Misinformation Warning Labels on Social MediaDisinformation in Open Online Media10.1007/978-3-031-71210-4_4(46-69)Online publication date: 31-Aug-2024
https://doi.org/10.1007/978-3-031-71210-4_4
Atanasova PAtanasova P(2024)Recent Developments on Accountability and Explainability for Complex Reasoning TasksAccountable and Explainable Methods for Complex Reasoning over Text10.1007/978-3-031-51518-7_9(191-199)Online publication date: 6-Apr-2024
https://doi.org/10.1007/978-3-031-51518-7_9

Index Terms

XAI in Automated Fact-Checking? The Benefits Are Modest and There's No One-Explanation-Fits-All
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
Abstract
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to ...
Highlights
- A novel four-axis framework to examine a model for robustness and explainability.
- Formulation of research questions at each axis and its corresponding taxonomy.
- Discussion of different explainability assessment methods.
- A novel ...

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OzCHI '23: Proceedings of the 35th Australian Computer-Human Interaction Conference

December 2023

733 pages

ISBN:9798400717079

DOI:10.1145/3638380

Copyright © 2023 ACM.

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Published: 10 May 2024

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OzCHI 2023

OzCHI 2023: OzCHI 2023

December 2 - 6, 2023

Wellington, New Zealand

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Overall Acceptance Rate 362 of 729 submissions, 50%

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Gruzd AMai PSoares F(2024)To Share or Not to Share: Randomized Controlled Study of Misinformation Warning Labels on Social MediaDisinformation in Open Online Media10.1007/978-3-031-71210-4_4(46-69)Online publication date: 31-Aug-2024
https://doi.org/10.1007/978-3-031-71210-4_4
Atanasova PAtanasova P(2024)Recent Developments on Accountability and Explainability for Complex Reasoning TasksAccountable and Explainable Methods for Complex Reasoning over Text10.1007/978-3-031-51518-7_9(191-199)Online publication date: 6-Apr-2024
https://doi.org/10.1007/978-3-031-51518-7_9

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