All Work Published on Privacy, Safety, Security
This brief reviews the history of algorithm auditing, describes its current state, and offers best practices for conducting algorithm audits today.
This brief reviews the history of algorithm auditing, describes its current state, and offers best practices for conducting algorithm audits today.
Stanford HAI joined global leaders to discuss the balance between AI innovation and safety and explore future policy paths.
Stanford HAI joined global leaders to discuss the balance between AI innovation and safety and explore future policy paths.
Building a National AI Research Resource: A Blueprint for the National Research Cloud
This white paper proposes a blueprint for a National Research Cloud that would enable much greater access to—and in that sense, democratize—forms of AI and AI research that have increased in computational demands.
This white paper proposes a blueprint for a National Research Cloud that would enable much greater access to—and in that sense, democratize—forms of AI and AI research that have increased in computational demands.
Can AI Hold Consistent Values? Stanford Researchers Probe LLM Consistency and Bias
New research tests large language models for consistency across diverse topics, revealing that while they handle neutral topics reliably, controversial issues lead to varied answers.
New research tests large language models for consistency across diverse topics, revealing that while they handle neutral topics reliably, controversial issues lead to varied answers.
Domain Shift and Emerging Questions in Facial Recognition Technology
This brief urges transparent, verifiable standards for facial-recognition systems and calls for a moratorium on government use until rigorous in-domain testing frameworks are established.
This brief urges transparent, verifiable standards for facial-recognition systems and calls for a moratorium on government use until rigorous in-domain testing frameworks are established.
At a recent Stanford-MIT-Princeton workshop, experts highlight the need for legal protections, standardized evaluation practices, and better terminology to support third-party AI evaluations.
At a recent Stanford-MIT-Princeton workshop, experts highlight the need for legal protections, standardized evaluation practices, and better terminology to support third-party AI evaluations.
