The development of privacy-enhancing technologies has made immense progress in reducing trade-offs between privacy and performance in data exchange and analysis. Similar tools for structured transparency could be useful for AI governance by offering capabilities such as external scrutiny, auditing, and source verification. It is useful to view these different AI governance objectives as a system of information flows in order to avoid partial solutions and significant gaps in governance, as there may be significant overlap in the software stacks needed for the AI governance use cases mentioned in this text. When viewing the system as a whole, the importance of interoperability between these different AI governance solutions becomes clear. Therefore, it is imminently important to look at these problems in AI governance as a system, before these standards, auditing procedures, software, and norms settle into place.

翻译：隐私增强技术的发展在减少数据交换与分析中隐私与性能之间的权衡方面取得了巨大进展。类似的结构化透明工具可通过提供外部审查、审计和源验证等功能，为人工智能治理提供支持。为避免碎片化解决方案及治理中的重大缺口，有必要将这些不同的人工智能治理目标视为一个信息流系统，因为文中提到的人工智能治理用例所需的软件栈可能存在显著重叠。从系统整体视角来看，这些不同人工智能治理解决方案之间的互操作性重要性愈发凸显。因此，在这些标准、审计程序、软件及规范定型之前，将人工智能治理中的这些问题视为一个系统来审视具有迫切的现实意义。