Drew Kelly
About
Advancing AI and data science research, education, policy, and practice to improve the human condition
Advancing AI and data science research, education, policy, and practice to improve the human condition
The Stanford Institute for Human-Centered AI (HAI) is an interdisciplinary institute that advances AI and data science research, education, policy, and practice. Launched in 2019, HAI draws on faculty and students from all seven of Stanford’s schools — business, education, engineering, humanities and sciences, law, medicine, and sustainability — along with partners across industry, government, and civil society, all united by the idea that AI should enable human flourishing.
The most important questions in AI do not respect disciplinary boundaries. They require experts across fields to work together at a scale that individual labs or departments cannot achieve alone. Stanford HAI brings together the interdisciplinary people, expertise, and shared capabilities that make this work possible.
AI is a civilization-changing technology. It will change how we work, communicate, learn, create, and make decisions, reshaping individual lives, institutions, and society itself. Stanford HAI was created in anticipation of this moment, and our work is organized around a conviction that shapes everything we do: AI should be guided by its human impact (H), designed to augment rather than replace people (A), and inspired by human intelligence (I).
We pursue that mission through three pillars — accelerating discovery, transforming education for human flourishing, and understanding and shaping AI for the good of society — each grounded in a cross-cutting commitment to openness. Open science, open source, and open class are what universities uniquely contribute to a rapidly evolving field. We publish in open forums, we release our models, tools, and datasets, and we make our teaching accessible worldwide.
AI is a general purpose technology that can be used for good and for ill. Our vision for the future is led by a commitment to human-centered uses of AI and data science and to ensuring that the benefits are broadly shared. In support of these goals, our research falls into three pillars: AI and data science for accelerating discovery, transforming education, and understanding and shaping AI for societal good.
Open scholarship is academia’s core advantage. HAI begins with openness — in the research we conduct and in the models, datasets, software, benchmarks, and educational resources we share. It takes three forms across everything we do.
Open science. Shared datasets, benchmarks, and open publications — including the work of establishing a science of AI measurement, with methods and standards the whole field can use.
Open source. Software, data, models, and infrastructure that serve the broader ecosystem. We build genuinely open-source, domain-specific models, supported by open-source specialists on staff.
Open class. K–12, university, policy, and executive education that translates Stanford expertise worldwide.
None of this works in isolation, so we build collaborative networks with aligned organizations globally. Shaping AI for humanity cannot mean shaping it for one region or one worldview.
The Stanford HAI leadership share a vision of AI and data science serving the collective needs of humanity. It is this goal that guides the work of HAI.
Human-Centered AI is about augmenting rather than replacing humans so that these technologies can enhance what makes us fundamentally human — our empathy, creativity, judgment, and wisdom.
AI is a civilizational technology. HAI serves as a lighthouse for human-centered AI — expanding the frontiers of knowledge and reimagining how we learn, use and govern this technology, with human dignity and agency as our North Star.
The first AI Index launches, establishing a global benchmark for tracking the progress and impact of artificial intelligence.
Stanford launches the Stanford Data Science Initiative and the inaugural cohort of Data Science Scholars to accelerate interdisciplinary data science research, education, and community building across the university.
Stanford pilots the Data Science for Social Good fellowship program.
Stanford HAI officially launches in March.
HAI proposes a National AI Research Resource (NAIRR) to strengthen U.S. AI innovation.
HAI launches its Faculty Fellows program.
HAI establishes its Seed Grants Program to fund innovative AI research.
SDS establishes its first interdisciplinary research center, the Center for Open and Reproducible Science (CORES).
Economist Erik Brynjolfsson joins HAI and launches the Stanford Digital Economy Lab (S-DEL).
HAI awards its first Hoffman-Yee Research Grants to support high-impact AI research addressing real-world challenges.
HAI, Stanford Computer Science, and the McCoy Family Center for Ethics in Society launch the Embedded EthiCS initiative.
HAI pilots the Ethics and Society Review (ESR) process for research grants.
The Center for Research on Foundation Models (CRFM), led by Percy Liang, launches alongside the landmark On the Opportunities and Risks of Foundation Models report.
HAI launches its Graduate Fellowship Program.
Stanford scholars publish a blueprint for a National AI Research Resource.
Peter Norvig joins HAI as Distinguished Education Fellow.
SDS launches the Stanford Causal Science Center (SC²) and the Center for Sustainability Data Science (SuDS).
SDS launches OpenSource@Stanford the organizational hub that coordinates, facilitates, and supports open source activities at Stanford.
HAI launches its Congressional Bootcamp to educate lawmakers on AI.
HAI introduces Student Affinity Groups and launches its AI Startup Series.
CRFM launches the Holistic Evaluation of Language Models (HELM).
HAI launches its Corporate Affiliate Program with Accenture as its founding member.
SDS launches the Stanford Data Science for Health Center
HAI launches its Tech Ethics & Policy Fellowship program.
Stanford Medicine and HAI launch RAISE-Health.
Fei-Fei Li and Rob Reich meet with President Biden to discuss U.S. AI leadership.
Fei-Fei Li joins the United Nations Secretary-General's Scientific Advisory Board.
HAI develops its AI Training Series for Federal Employees in partnership with the White House.
CRFM creates the Foundation Model Transparency Index (FMTI).
HAI and The Asia Foundation launch AI Perspectives from Asia.
SDS builds and deploys Marlowe, Stanford's GPU-centric supercomputer, providing advanced computing resources for data science and AI research.
Growth of flagship conferences and workshops, including the Physics + AI (PAI) conference, Rising Stars in Data Science, the CORES Symposium, and Causal Science Conferences.
HAI co-hosts the Joint California Summit on Generative AI with California state leaders and UC Berkeley.
HAI and the Stanford d.school launch a program on designing human-centered AI for the social sector.
HAI expands responsible AI research through collaboration with the Stanford Robotics Center.
SDS partners with KIPAC to establish the Center for Decoding the Universe (C4DU) and with the Wu Tsai Institute to launch the Center for Neural Data Science.
Guido W. Imbens named director of Stanford Data Science.
Yejin Choi joins Stanford as the Dieter Schwarz Foundation HAI Professor and Senior Fellow.
Stanford AI Lab (SAIL) joins forces with HAI.
Fei-Fei Li receives the Queen Elizabeth Prize for Engineering.
HAI and SDS unite under the HAI name to become the front door of AI and data science at Stanford.
James Landay becomes HAI's Denning Director.
Founding Director Fei-Fei Li becomes Special Advisor on AI to Stanford President Jonathan Levin.
The Stanford Center for Computational Market Design joins Stanford Data Science.
HAI and the Swiss National AI Institute announce a strategic alliance to advance open, human-centered AI.
HAI launches the AI+Orgs Lab, led by Faculty Director Melissa Valentine.
Learn about the Stanford Institute for Human-Centered AI (HAI) and our mission through the voices of our leaders.
The 2024-25 year marked a significant period of growth as we continued to advance the mission of human-centered AI. Government leaders, industry executives, educators, and civil society organizations have come to HAI for guidance on both technical insight and a deeper understanding of what AI means for their communities and institutions.
AI can accelerate scholarship in every discipline — and domain-specific scholarship is itself key for advancing AI. We work to catalyze that virtuous cycle.
Better AI makes better scholarship possible. Less obviously, the reverse is also true: the hardest problems in, for example, astronomy, medicine, economics, and the humanities expose the limits of current models and point toward what AI needs to become. HAI is built to close that loop.
Our work here has two dimensions. AI-driven discovery advances every discipline, where domain-specific scholarship can itself become a powerful vector for advancing AI — from decoding the physical universe from data, to understanding how ideas, language, and cultures evolve, to designing environments and therapies for human health. Our research on Foundations of AI pursues new architectures, algorithms, and evaluation approaches that go beyond simply adding silicon to scale models, including the search for shared principles underlying natural and artificial intelligence.
As AI reshapes work and society, what should people learn, and how should they learn it? We are building the rigorous evidence base to answer these questions.
Most conversations about AI in education begin and end with how AI transforms schooling. We start from a broader goal: supporting human flourishing in the age of AI. That reframing changes the research agenda.
We ask what knowledge and skills learners of all ages actually need as AI reshapes work, civic life, and the professions. We also focus on how to learn: developing AI-enabled learning systems that genuinely support students, teachers, communities, and institutions.
Answering these questions requires evidence gathered over time. We aim to build longitudinal research infrastructure — with Stanford’s own courses serving as a testbed — to track learner outcomes through their time at Stanford and well beyond graduation. This work extends from K–12 through university and lifelong learning.
AI is scaling at a breakneck pace while public trust in its governance remains unsettled. HAI offers a rigorous, empirically grounded alternative to the extremes dominating public conversation.
To shape AI for positive societal impact, you need to understand both how the systems work and how they transform incentives, institutions, and human lives. That requires technical and humanistic expertise working in dialogue — not going along parallel tracks. That integration is what Stanford HAI was built to provide.
Our research fuses technical AI expertise, design, and humanistic inquiry to study AI systems as they actually behave in the world and to develop new methods and tools to design them for the better. Then we translate that scholarship into clear, trusted guidance that leaders can depend upon to make decisions as AI continues to evolve.
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