Regulation, Policy, Governance
How policymakers can best regulate AI to balance innovation with public interests and human rights.
Elon Musk was forced to put restrictions on X and its AI chatbot, Grok, after its image generator sparked outrage around the world. Grok created non-consensual sexualized images, prompting some countries to ban the bot. Liz Landers discussed Grok's troubles with Riana Pfefferkorn of the Stanford Institute for Human-Centered Artificial Intelligence.
Elon Musk was forced to put restrictions on X and its AI chatbot, Grok, after its image generator sparked outrage around the world. Grok created non-consensual sexualized images, prompting some countries to ban the bot. Liz Landers discussed Grok's troubles with Riana Pfefferkorn of the Stanford Institute for Human-Centered Artificial Intelligence.
The AI Arms Race In Health Insurance Utilization Review: Promises Of Efficiency And Risks Of Supercharged Flaws
Health insurers and health care provider organizations are increasingly using artificial intelligence (AI) tools in prior authorization and claims processes. AI offers many potential benefits, but its adoption has raised concerns about the role of the “humans in the loop,” users’ understanding of AI, opacity of algorithmic determinations, underperformance in certain tasks, automation bias, and unintended social consequences. To date, institutional governance by insurers and providers has not fully met the challenge of ensuring responsible use. However, several steps could be taken to help realize the benefits of AI use while minimizing risks. Drawing on empirical work on AI use and our own ethical assessments of provider-facing tools as part of the AI governance process at Stanford Health Care, we examine why utilization review has attracted so much AI innovation and why it is challenging to ensure responsible use of AI. We conclude with several steps that could be taken to help realize the benefits of AI use while minimizing risks.
Health insurers and health care provider organizations are increasingly using artificial intelligence (AI) tools in prior authorization and claims processes. AI offers many potential benefits, but its adoption has raised concerns about the role of the “humans in the loop,” users’ understanding of AI, opacity of algorithmic determinations, underperformance in certain tasks, automation bias, and unintended social consequences. To date, institutional governance by insurers and providers has not fully met the challenge of ensuring responsible use. However, several steps could be taken to help realize the benefits of AI use while minimizing risks. Drawing on empirical work on AI use and our own ethical assessments of provider-facing tools as part of the AI governance process at Stanford Health Care, we examine why utilization review has attracted so much AI innovation and why it is challenging to ensure responsible use of AI. We conclude with several steps that could be taken to help realize the benefits of AI use while minimizing risks.
Response to OSTP's Request for Information on Accelerating the American Scientific Enterprise
Stanford scholars respond to a federal RFI on scientific discovery, calling for the government to support a new “team science” academic research model for AI-enabled discovery.
Stanford scholars respond to a federal RFI on scientific discovery, calling for the government to support a new “team science” academic research model for AI-enabled discovery.
Scholars develop a framework in collaboration with luxury goods multinational LVMH that lays out how large companies can flexibly deploy principles on the responsible use of AI across business units worldwide.
Scholars develop a framework in collaboration with luxury goods multinational LVMH that lays out how large companies can flexibly deploy principles on the responsible use of AI across business units worldwide.
All Work Published on Regulation, Policy, Governance
Riana Pfefferkorn, Policy Fellow at HAI, urges immediate Congressional hearings to scope a legal safe harbor for AI-generated child sexual abuse materials following a recent scandal with Grok's newest generative image features.
Riana Pfefferkorn, Policy Fellow at HAI, urges immediate Congressional hearings to scope a legal safe harbor for AI-generated child sexual abuse materials following a recent scandal with Grok's newest generative image features.
AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence
Response to FDA's Request for Comment on AI-Enabled Medical Devices
Stanford scholars respond to a federal RFC on evaluating AI-enabled medical devices, recommending policy interventions to help mitigate the harms of AI-powered chatbots used as therapists.
Stanford scholars respond to a federal RFC on evaluating AI-enabled medical devices, recommending policy interventions to help mitigate the harms of AI-powered chatbots used as therapists.
Daniel E. Ho
HAI Policy Fellow Riana Pfefferkorn discusses the policy implications of the "mass digital undressing spree,” where the chatbot Grok responded to user prompts to remove the clothing from images of women and pose them in bikinis and to create "sexualized images of children" and post them on X.
HAI Policy Fellow Riana Pfefferkorn discusses the policy implications of the "mass digital undressing spree,” where the chatbot Grok responded to user prompts to remove the clothing from images of women and pose them in bikinis and to create "sexualized images of children" and post them on X.
Automated real-time assessment of intracranial hemorrhage detection AI using an ensembled monitoring model (EMM)
Artificial intelligence (AI) tools for radiology are commonly unmonitored once deployed. The lack of real-time case-by-case assessments of AI prediction confidence requires users to independently distinguish between trustworthy and unreliable AI predictions, which increases cognitive burden, reduces productivity, and potentially leads to misdiagnoses. To address these challenges, we introduce Ensembled Monitoring Model (EMM), a framework inspired by clinical consensus practices using multiple expert reviews. Designed specifically for black-box commercial AI products, EMM operates independently without requiring access to internal AI components or intermediate outputs, while still providing robust confidence measurements. Using intracranial hemorrhage detection as our test case on a large, diverse dataset of 2919 studies, we demonstrate that EMM can successfully categorize confidence in the AI-generated prediction, suggest appropriate actions, and help physicians recognize low confidence scenarios, ultimately reducing cognitive burden. Importantly, we provide key technical considerations and best practices for successfully translating EMM into clinical settings.
Artificial intelligence (AI) tools for radiology are commonly unmonitored once deployed. The lack of real-time case-by-case assessments of AI prediction confidence requires users to independently distinguish between trustworthy and unreliable AI predictions, which increases cognitive burden, reduces productivity, and potentially leads to misdiagnoses. To address these challenges, we introduce Ensembled Monitoring Model (EMM), a framework inspired by clinical consensus practices using multiple expert reviews. Designed specifically for black-box commercial AI products, EMM operates independently without requiring access to internal AI components or intermediate outputs, while still providing robust confidence measurements. Using intracranial hemorrhage detection as our test case on a large, diverse dataset of 2919 studies, we demonstrate that EMM can successfully categorize confidence in the AI-generated prediction, suggest appropriate actions, and help physicians recognize low confidence scenarios, ultimately reducing cognitive burden. Importantly, we provide key technical considerations and best practices for successfully translating EMM into clinical settings.