TrustME:一个上下文意识的可解释性模型,以促进用户对指导的信任.
IEEE transactions on visualization and computer graphics
|April 21, 2025
概括
可解释的指导可以提高用户对数据分析系统的信任. 对系统干预的明确解释对于有效的指导至关重要,改善用户-系统关系和视觉分析中的数据解释.
科学领域:
- 人与计算机的交互
- 信息可视化 信息可视化
- 可解释的人工智能
背景情况:
- 通过指导增强的视觉分析 (VA) 帮助用户进行数据解释.
- 缺乏对系统干预的明确解释可能会降低指导的有效性.
- 有效的指导需要具体的上下文解释,以建立用户的信任.
研究的目的:
- 在VA中引入可解释指导的概念.
- 为VA指导提出一个设计可解释性策略的模型.
- 调查可解释性指导对用户信任和系统采用的影响.
主要方法:
- 关于可解释的人工智能,VA指导和用户-系统信任的文献综述.
- 开发一个模型来设计可解释的指导策略.
- 将模型应用于两个VA场景,并与专家进行设计介绍.
主要成果:
- 拟议的模型有助于设计VA指导的上下文意识解释.
- 可解释的指导积极影响用户对VA系统的信任.
- 该模型有助于设计人员阐明系统干预背后的逻辑.
结论:
- 可解释的指导对于有效的VA系统至关重要.
- 拟议的模型为设计可靠和有效的指导提供了一个框架.
- 未来的研究应该在不同的VA环境中探索可解释的指导策略的经验验证.
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