解密XAI:对可理解的XAI解释的要求
Jan Stodt1, Christoph Reich1, Martin Knahl1
1Institute for Data Science, Cloud Computing, and IT Security, Furtwangen University, Furtwangen, Germany.
Studies in health technology and informatics
|August 23, 2024
概括
本研究概述了可用于非专家的可解释人工智能 (XAI) 的要求,重点关注医疗保健专业人员. 它详细介绍了如何优化认知负载,性能和信任,以实现有效的人类-人工智能协作.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 医疗保健信息学 医疗保健信息学
背景情况:
- 可解释的人工智能 (XAI) 方法需要针对非AI专家量身定制的可用性评估.
- 医疗保健专业人员需要透明和可理解的AI解释,以便有效地做出临床决策.
- 当前的XAI方法可能无法充分满足高风险领域的最终用户的认知和信任需求.
研究的目的:
- 为评估XAI方法的可用性建立明确的要求.
- 引导设计XAI解释,这些解释对于非专家用户来说是可以理解和值得信赖的.
- 在医疗保健等领域促进无的人类-人工智能合作.
主要方法:
- 关于XAI可用性的现有文献的综合.
- 分析与人工智能解释的用户交互相关的经验发现.
- 确定影响用户理解,信任和性能的关键因素.
主要成果:
- 最佳的认知负载,任务性能和任务时间对于XAI可用性至关重要.
- 根据用户的专业知识量身定制解释,整合领域知识,使用非命题表示,提高理解力.
- 相关性,准确性和真实性对于建立用户对XAI系统的信任至关重要.
结论:
- 有效的XAI解释必须是透明的,用户友好的,并意识到上下文.
- 提供了用于设计可用的XAI的实用指南,特别是用于医疗保健应用.
- 这项工作通过改进XAI设计,为推进人类-人工智能协作做出了重要贡献.
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