All Work Published on Privacy, Safety, Security
Stanford scholars respond to a federal RFC on the U.S. AI Safety Institute’s draft guidelines for managing the misuse risk for dual-use foundation models.
Stanford scholars respond to a federal RFC on the U.S. AI Safety Institute’s draft guidelines for managing the misuse risk for dual-use foundation models.
From Privacy to ‘Glass Box’ AI, Stanford Students Are Targeting Real-World Problems
An Amazon-backed fellowship will support 10 Stanford PhD students whose work explores everything from how we communicate to understanding disease and protecting our data.
An Amazon-backed fellowship will support 10 Stanford PhD students whose work explores everything from how we communicate to understanding disease and protecting our data.
Stanford scholars respond to a federal RFC on dual use foundation models with widely available model weights, urging policymakers to consider their marginal risks.
Stanford scholars respond to a federal RFC on dual use foundation models with widely available model weights, urging policymakers to consider their marginal risks.
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We need to rethink student assessment, AI literacy, and technology’s usefulness, according to experts at the recent AI+Education Summit.
We need to rethink student assessment, AI literacy, and technology’s usefulness, according to experts at the recent AI+Education Summit.
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Rethinking Privacy in the AI Era: Policy Provocations for a Data-Centric World
This white paper explores the current and future impact of privacy and data protection legislation on AI development and provides recommendations for mitigating privacy harms in an AI era.
This white paper explores the current and future impact of privacy and data protection legislation on AI development and provides recommendations for mitigating privacy harms in an AI era.
A Stanford HAI workshop brought together experts to develop new evaluation methods that assess AI's hidden capabilities, not just its test-taking performance.
A Stanford HAI workshop brought together experts to develop new evaluation methods that assess AI's hidden capabilities, not just its test-taking performance.
