Exploring the Relevance of Data Privacy-Enhancing Technologies for AI Governance Use Cases
It addresses the problem of fragmented AI governance solutions for policymakers and developers, but is incremental as it builds on existing privacy technologies.
The paper explores how privacy-enhancing technologies can be applied to AI governance to enable external scrutiny and auditing, emphasizing the need for a systemic approach to avoid gaps and ensure interoperability.
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.