{"title":"基于实例的 XAI 对神经网络的信任、理解和性能的影响","authors":"Maya Perlmutter, Ryan Gifford, Samantha Krening","doi":"10.1016/j.ijhcs.2024.103277","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"188 ","pages":"Article 103277"},"PeriodicalIF":5.3000,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Impact of example-based XAI for neural networks on trust, understanding, and performance\",\"authors\":\"Maya Perlmutter, Ryan Gifford, Samantha Krening\",\"doi\":\"10.1016/j.ijhcs.2024.103277\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":54955,\"journal\":{\"name\":\"International Journal of Human-Computer Studies\",\"volume\":\"188 \",\"pages\":\"Article 103277\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Human-Computer Studies\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1071581924000612\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, CYBERNETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Human-Computer Studies","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1071581924000612","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
Impact of example-based XAI for neural networks on trust, understanding, and performance
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.
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
...