Impact of example-based XAI for neural networks on trust, understanding, and performance

IF 5.3 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies Pub Date : 2024-04-17 DOI:10.1016/j.ijhcs.2024.103277

Maya Perlmutter, Ryan Gifford, Samantha Krening

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引用次数: 0

Abstract

The purpose of this study is to examine the impact of an example-based explainable artificial intelligence (XAI) interface on trust, understanding, and performance in highly-technical populations. XAI studies often focus on general users in low-risk domains. This study examined the impact of showing the closest matches from the training data from two classes on trust, understanding, and performance for highly-technical users in a high-risk domain. We found that providing example-based explanations significantly increased trust and understanding without decreasing performance. Showing the most similar examples from two classes increased trust more than showing examples from only one class. Participants did not treat different classes the same. The most important features for predicting how well an interface was understood were the helpfulness of the provided examples and the person's trust in the human-machine team. We found priming of highly-technical participants to be particularly important for running XAI studies to mitigate the fear of their jobs being impacted.

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基于实例的 XAI 对神经网络的信任、理解和性能的影响

本研究旨在考察基于示例的可解释人工智能（XAI）界面对高技术人群的信任、理解和绩效的影响。XAI 研究通常侧重于低风险领域的普通用户。本研究考察了在高风险领域中，显示来自两个类别的训练数据中最接近的匹配结果对高技术用户的信任、理解和表现的影响。我们发现，提供基于示例的解释能显著提高信任度和理解力，而不会降低绩效。展示两个类别中最相似的示例比只展示一个类别的示例更能提高信任度。参与者对待不同类别的态度并不相同。预测界面理解程度的最重要特征是所提供示例的有用性和参与者对人机团队的信任度。我们发现，在进行 XAI 研究时，对高技术参与者进行引导尤其重要，这样可以减轻他们对工作受到影响的恐惧。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Human-Computer Studies 工程技术-计算机：控制论

CiteScore

11.50

自引率

5.60%

发文量

108

审稿时长

3 months

期刊介绍： The International Journal of Human-Computer Studies publishes original research over the whole spectrum of work relevant to the theory and practice of innovative interactive systems. The journal is inherently interdisciplinary, covering research in computing, artificial intelligence, psychology, linguistics, communication, design, engineering, and social organization, which is relevant to the design, analysis, evaluation and application of innovative interactive systems. Papers at the boundaries of these disciplines are especially welcome, as it is our view that interdisciplinary approaches are needed for producing theoretical insights in this complex area and for effective deployment of innovative technologies in concrete user communities. Research areas relevant to the journal include, but are not limited to: • Innovative interaction techniques • Multimodal interaction • Speech interaction • Graphic interaction • Natural language interaction • Interaction in mobile and embedded systems • Interface design and evaluation methodologies • Design and evaluation of innovative interactive systems • User interface prototyping and management systems • Ubiquitous computing • Wearable computers • Pervasive computing • Affective computing • Empirical studies of user behaviour • Empirical studies of programming and software engineering • Computer supported cooperative work • Computer mediated communication • Virtual reality • Mixed and augmented Reality • Intelligent user interfaces • Presence ...