All Work Published on Regulation, Policy, Governance
Response to the Department of Education’s Request for Information on AI in Education
Stanford scholars respond to a federal RFI on advancing AI in education, urging policymakers to anchor their approach in proven research.
Stanford scholars respond to a federal RFI on advancing AI in education, urging policymakers to anchor their approach in proven research.
"The biggest difference between 23andMe and other breaches is that sequenced DNA is 'irreplaceable and immutable,' said Jennifer King," a Stanford HAI Policy Fellow.
"The biggest difference between 23andMe and other breaches is that sequenced DNA is 'irreplaceable and immutable,' said Jennifer King," a Stanford HAI Policy Fellow.
Conditional Generative Models for Synthetic Tabular Data: Applications for Precision Medicine and Diverse Representations
Tabular medical datasets, like electronic health records (EHRs), biobanks, and structured clinical trial data, are rich sources of information with the potential to advance precision medicine and optimize patient care. However, real-world medical datasets have limited patient diversity and cannot simulate hypothetical outcomes, both of which are necessary for equitable and effective medical research. Fueled by recent advancements in machine learning, generative models offer a promising solution to these data limitations by generating enhanced synthetic data. This review highlights the potential of conditional generative models (CGMs) to create patient-specific synthetic data for a variety of precision medicine applications. We survey CGM approaches that tackle two medical applications: correcting for data representation biases and simulating digital health twins. We additionally explore how the surveyed methods handle modeling tabular medical data and briefly discuss evaluation criteria. Finally, we summarize the technical, medical, and ethical challenges that must be addressed before CGMs can be effectively and safely deployed in the medical field.
Tabular medical datasets, like electronic health records (EHRs), biobanks, and structured clinical trial data, are rich sources of information with the potential to advance precision medicine and optimize patient care. However, real-world medical datasets have limited patient diversity and cannot simulate hypothetical outcomes, both of which are necessary for equitable and effective medical research. Fueled by recent advancements in machine learning, generative models offer a promising solution to these data limitations by generating enhanced synthetic data. This review highlights the potential of conditional generative models (CGMs) to create patient-specific synthetic data for a variety of precision medicine applications. We survey CGM approaches that tackle two medical applications: correcting for data representation biases and simulating digital health twins. We additionally explore how the surveyed methods handle modeling tabular medical data and briefly discuss evaluation criteria. Finally, we summarize the technical, medical, and ethical challenges that must be addressed before CGMs can be effectively and safely deployed in the medical field.
This brief evaluates the impact of authorship labels on the persuasiveness of AI-written policy messages.
This brief evaluates the impact of authorship labels on the persuasiveness of AI-written policy messages.
A Stanford study reveals that leading AI companies are pulling user conversations for training, highlighting privacy risks and a need for clearer policies.
A Stanford study reveals that leading AI companies are pulling user conversations for training, highlighting privacy risks and a need for clearer policies.
Adverse Event Reporting for AI: Developing the Information Infrastructure Government Needs to Learn and Act
This brief assesses the benefits of and provides policy recommendations for adverse event reporting systems for AI that report failures and harms post deployment.
This brief assesses the benefits of and provides policy recommendations for adverse event reporting systems for AI that report failures and harms post deployment.
