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

Exploring the Relevance of Data Privacy-Enhancing Technologies for AI Governance Use Cases