The paper argues that standardized privacy benchmarks are needed to evaluate privacy risks in frontier AI models, including data memorization, inference of sensitive attributes, and over‑collection. It describes emerging efforts such as the MLCommons Privacy and Confidentiality Working Group developing a privacy risk taxonomy and benchmarks for sensitive information disclosure and data minimization. The authors call for industry‑wide adoption of such benchmarks to help developers, deployers, regulators, and researchers assess and compare AI privacy performance.
Why it matters: Standardized privacy benchmarks would give concrete, comparable metrics for AI developers and deployers, supporting better privacy protection and regulatory oversight.
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