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极大地减少了数据交换与分析中隐私与性能之间的权衡。类似的结构化透明度工具可通过提供外部审查、审计及来源验证等功能，为人工智能治理提供支持。为避免出现碎片化解决方案及治理中的重大缺口，有必要将不同的人工智能治理目标视为信息流系统来审视——因为本文所述人工智能治理用例所需的软件栈可能存在显著重叠。当从系统整体角度考量时，此类不同人工智能治理解决方案间的互操作性重要性便不言而喻。因此，在这些标准、审计程序、软件及规范尚未固化之前，亟需以系统性眼光审视人工智能治理中的这些问题。