Stanford students across disciplines are teaming up to tackle society’s pressing questions in the age of AI.
Stanford students across disciplines are teaming up to tackle society’s pressing questions in the age of AI.
Operationalizing Real-Time Monitoring of Clinical AI
This brief demonstrates how real-time monitoring can address critical gaps in the oversight of radiological AI tools.
This brief demonstrates how real-time monitoring can address critical gaps in the oversight of radiological AI tools.
PsychAdapter lets researchers dial in on personality traits, age, and mental health characteristics to generate text that sounds like real individuals, opening the door to training simulations and personalized content.
PsychAdapter lets researchers dial in on personality traits, age, and mental health characteristics to generate text that sounds like real individuals, opening the door to training simulations and personalized content.
This brief examines the privacy risks foundation models pose to individuals and society, and governance mechanisms needed to address them.
This brief examines the privacy risks foundation models pose to individuals and society, and governance mechanisms needed to address them.
Reading Today’s Headlines Through AI: A Real-Time Audit of Six Commercial Chatbots
In a new study, scholars measured how accurately popular AI chatbots answered questions about the emerging news and found substantial regional disparity, dependence on distinct information ecosystems, and acute fragility under imperfect prompts.
In a new study, scholars measured how accurately popular AI chatbots answered questions about the emerging news and found substantial regional disparity, dependence on distinct information ecosystems, and acute fragility under imperfect prompts.
This white paper analyzes the varying ways AI tools can advance language digitization work, and provides recommendations for responsibly realizing the potential of AI in supporting the digital inclusion of digitally disadvantaged languages.
This white paper analyzes the varying ways AI tools can advance language digitization work, and provides recommendations for responsibly realizing the potential of AI in supporting the digital inclusion of digitally disadvantaged languages.
