Machine Learning
Learn about the latest advances in machine learning that allow systems to learn and improve over time.
Special Seminar and Fireside Chat: Understanding Work Using AI
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
Two models working together perform worse than one alone, exposing a critical gap in artificial intelligence capabilities.
Two models working together perform worse than one alone, exposing a critical gap in artificial intelligence capabilities.
Human-Centered Large Language Models
Large language models (LLM) have moved from research laboratories into the infrastructure of everyday life. They power everything from developer tools to educational tutors, healthcare assistants to enterprise agents. Yet the frameworks guiding LLM development remain anchored in technical performance metrics that tell us little about whether these systems actually benefit the people who use them.
Large language models (LLM) have moved from research laboratories into the infrastructure of everyday life. They power everything from developer tools to educational tutors, healthcare assistants to enterprise agents. Yet the frameworks guiding LLM development remain anchored in technical performance metrics that tell us little about whether these systems actually benefit the people who use them.
We invited 11 sci-fi filmmakers and AI researchers to Stanford for Stories for the Future, a day-and-a-half experiment in fostering new narratives about AI. Researchers shared perspectives on AI and filmmakers reflected on the challenges of writing AI narratives. Together researcher-writer pairs transformed a research paper into a written scene. The challenge? Each scene had to include an AI manifestation, but could not be about the personhood of AI or AI as a threat. Read the results of this project.
We invited 11 sci-fi filmmakers and AI researchers to Stanford for Stories for the Future, a day-and-a-half experiment in fostering new narratives about AI. Researchers shared perspectives on AI and filmmakers reflected on the challenges of writing AI narratives. Together researcher-writer pairs transformed a research paper into a written scene. The challenge? Each scene had to include an AI manifestation, but could not be about the personhood of AI or AI as a threat. Read the results of this project.
Improving Transparency in AI Language Models: A Holistic Evaluation
This brief introduces Holistic Evaluation of Language Models (HELM) as a framework to evaluate commercial application of AI use cases.
This brief introduces Holistic Evaluation of Language Models (HELM) as a framework to evaluate commercial application of AI use cases.
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All Work Published on Machine Learning
Christopher Ong | Research Acceleration: The View Inside OpenAI
In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
Christopher Ong | Research Acceleration: The View Inside OpenAI
In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
AI Hiring Tools Can Yield Racial Bias and Systemic Rejection
The first large-scale study of hiring algorithms in the wild finds concerning patterns to how systems reject candidates.
The first large-scale study of hiring algorithms in the wild finds concerning patterns to how systems reject candidates.
Current societal trends reflect an increased mistrust in science and a lowered civic engagement that threaten to impair research that is foundational for ensuring public health and advancing health equity. One effective countermeasure to these trends lies in community-facing citizen science applications to increase public participation in scientific research, making this field an important target for artificial intelligence (AI) exploration. We highlight potentially promising citizen science AI applications that extend beyond individual use to the community level, including conversational large language models, text-to-image generative AI tools, descriptive analytics for analyzing integrated macro- and micro-level data, and predictive analytics. The novel adaptations of AI technologies for community-engaged participatory research also bring an array of potential risks. We highlight possible negative externalities and mitigations for some of the potential ethical and societal challenges in this field.
Current societal trends reflect an increased mistrust in science and a lowered civic engagement that threaten to impair research that is foundational for ensuring public health and advancing health equity. One effective countermeasure to these trends lies in community-facing citizen science applications to increase public participation in scientific research, making this field an important target for artificial intelligence (AI) exploration. We highlight potentially promising citizen science AI applications that extend beyond individual use to the community level, including conversational large language models, text-to-image generative AI tools, descriptive analytics for analyzing integrated macro- and micro-level data, and predictive analytics. The novel adaptations of AI technologies for community-engaged participatory research also bring an array of potential risks. We highlight possible negative externalities and mitigations for some of the potential ethical and societal challenges in this field.
Promoting Algorithmic Fairness in Clinical Risk Prediction
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This brief examines the debate on algorithmic fairness in clinical predictive algorithms and recommends paths to safer, more equitable healthcare AI.
This brief examines the debate on algorithmic fairness in clinical predictive algorithms and recommends paths to safer, more equitable healthcare AI.
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Ramesh Johari
From Benchmarks to Real-World Impact: Causal Science Conference Explores Modern Challenges in AI Evaluation
On April 24, 2026, the Stanford Causal Science Conference: Frontiers in AI Evaluation brought together leading researchers from academia and industry to examine how causal reasoning, evaluation, and measurement are reshaping the future of AI systems.
On April 24, 2026, the Stanford Causal Science Conference: Frontiers in AI Evaluation brought together leading researchers from academia and industry to examine how causal reasoning, evaluation, and measurement are reshaping the future of AI systems.
