视觉分析用于可解释和可靠的人工智能
IEEE computer graphics and applications
|June 12, 2025
概括
视觉分析 (VA) 通过将AI模型与交互式可视化相结合,增强对人工智能 (AI) 系统的信任. 这种方法使专家能够改进人工智能模型,提高其可靠性和在医疗保健等关键应用中采用.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 数据可视化 数据可视化
背景情况:
- 智能系统对于解决复杂问题至关重要,包括医疗诊断,但它们的不透明性阻碍了专家的信任和采用.
- 人工智能系统缺乏透明度,导致可靠性和融入关键领域的挑战,尽管人工智能有潜力改善结果和减少经济负担.
- 视觉分析 (VA) 提供了一种将AI与交互式可视化集成的方法,使专家能够提供意见并弥合AI和人类理解之间的差距.
研究的目的:
- 定义,分类和探索视觉分析 (VA) 解决方案如何促进对人工智能 (AI) 系统的信任.
- 为创新的可视化提供一个设计空间,以提高AI的透明度和可用性.
- 介绍开发的VA仪表板的概述,支持人工智能管道的各个阶段.
主要方法:
- 文献综述和AI中VA的概念框架开发.
- 探索VA技术,以提高AI模型的透明度和可解释性.
- 开发和展示VA仪表板用于AI管道阶段:数据处理,特征工程,超参数调整,模型理解,调试,改进和比较.
主要成果:
- 通过交互式可视化,VA有效地弥合了AI预测和人类专业知识之间的差距.
- 为创新的VA解决方案提出了一个设计空间,为开发信任建设工具提供了一个结构化的方法.
- 开发的VA仪表板展示了在整个AI生命周期中支持关键任务的实际应用,从数据准备到模型评估.
结论:
- 视觉分析是一种强大的方法,可以增加信任,并促进人工智能系统在各种领域的采用,特别是在医疗保健领域.
- 交互式可视化使领域专家能够理解,改进和验证人工智能模型,从而实现更可靠和值得信赖的智能系统.
- 拟议的VA设计空间和展示的仪表板为未来开发透明和以用户为中心的AI解决方案提供了基础.
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