管道(软件)
领域(数学分析)
接口(物质)
计算机科学
基线(sea)
人口
因子(编程语言)
人机交互
人工智能
知识管理
数学
政治学
人口学
数学分析
气泡
最大气泡压力法
社会学
并行计算
法学
程序设计语言
作者
Michael Perlmutter,Samantha Krening
标识
DOI:10.1177/21695067231192602
摘要
We examined the impact of explainable artificial intelligence on trust in a highly-technical population applied to a high-risk domain. Specifically, we examined the effect of an example-based explainable machine learning system on trust for data analysts working for a pipeline inspection company. This study compared a baseline interface with no explanation to two example-based explainable interfaces. We found that showing examples from multiple classes significantly increased trust compared to the other interfaces. Also, enabling the user to override the ML agent’s decision is a bigger factor for trust for this technical population than the amount of explanation shown in the interface.
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