我们需要更好的AI图像和更好的AI对话
Marc Steen1, Tjerk Timan1, Jurriaan Van Diggelen1
1TNO, The Hague, Netherlands.
概括
这项研究批评了常见的AI可视化,建议我们将人工智能 (AI) 视为社会技术系统的一部分. 这种转变促进了人工智能开发和部署的更好的人类控制,透明度和公平性.
科学领域:
- 社会技术系统 社会技术系统
- 人工智能 (AI) 伦理学 伦理学
- 网络学就是网络学.
背景情况:
- 当前的人工智能可视化通常将系统描述为独立实体 (例如机器人,电子大脑).
- 这些表述误导,认为人工智能在没有人类影响的情况下运作,并拥有优越的推理能力.
- 这忽视了人工智能的固有人类参与和社会背景.
研究的目的:
- 批评流行的人工智能可视化和话语.
- 为理解和讨论人工智能系统提出替代框架.
- 促进人工智能的有意义的人类控制,透明度和公平性.
主要方法:
- 批评当前的人工智能可视化和通信实践.
- 关于社会技术系统视角的建议,借鉴网络学.
- 引入"必要的多样性"和"边界对象"作为概念工具.
- 使用思想实验和实际例子.
主要成果:
- 常见的AI图像 (人形机器人,电子大脑) 是误导性的.
- 在社会技术系统中查看AI提供了更准确的表示.
- 扩展讨论超越传统的错误矩阵对于批判性分析至关重要.
- 边界对象有助于跨学科的合作.
结论:
- 采用社会技术视角可以增强对AI复杂性的理解.
- 需要的多样性和边界对象是有效的AI设计和部署的关键.
- 这些方法支持人工智能系统的有意义的人类控制,透明度和公平性.
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