Stanford Institute for Human-Centered Artificial Intelligence

09/02/2026

What role do we play in shaping AI's future?

Stanford HAI's annual conference returns this October with a full day of conversations on work, learning, governance, and the choices ahead. Speakers include Fei-Fei Li, Jeff Dean, Audrey Tang, Erik Brynjolfsson, Alondra Nelson, and Ion Stoica, with more to be announced.

Confronting Our AI Future: Hope, Fear, and the Choices Ahead will be a paid, in-person event at Stanford with expanded networking and discussion opportunities. Register by October 1 for early bird pricing: https://hai.stanford.edu/events/confronting-our-ai-future-hope-fear-and-the-choices-ahead

08/31/2026

Stanford HAI is hiring an Education Program Manager to help run its growing portfolio of education programs — including Data Science Scholars, AI4All, and Data Science for Social Good. The role spans day-to-day program operations, event logistics, participant onboarding, and coordination across faculty advisors, mentors, vendors, and partner organizations.

Learn more and apply:
https://careersearch.stanford.edu/hcmUI/CandidateExperience/en/sites/stanford/job/200482?keyword=Program+Manager+Education&mode=location

08/29/2026

Public attitudes toward AI will shape how governments regulate it, how firms invest, and how quickly new tools spread through workplaces.

Stanford researchers Michael Tomz and Diyi Yang are conducting the first systematic comparison of public opinion on AI in the United States and China — one of three projects receiving grants from Stanford HAI and the Hoover Institution's Technology Policy Accelerator.

Learn about all three projects:

Stanford HAI and the Hoover Institution’s Technology Policy Accelerator back projects examining AI's role in detecting nuclear proliferation, U.S.-China competition, and political influence.

08/28/2026

Every system built to detect child sexual abuse material online involves unavoidable trade-offs. Too sensitive, and innocent users are reported to authorities. Too lenient, and abuse goes undetected.

Despite near-universal agreement that this material is illegal, there is no uniform framework dictating what providers must do. That means the hardest choices — how to configure detection systems, whether to use human reviewers, how to handle false positives — fall to providers and the computing professionals building these systems.

Stanford HAI Policy Fellow Riana Pfefferkorn on the ethical weight of those decisions:

Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing.

08/27/2026

What does America's AI future look like — and is Washington ready for it?

That question was at the center of a remarkable conversation a few weeks ago at Stanford HAI, where NVIDIA CEO Jensen Huang, former 66th US Secretary of State and Director of the Hoover Institution Condoleezza Rice, and Stanford HAI founding director Fei-Fei Li joined congressional staffers for the keynote dinner of our Congressional Boot Camp on AI.

A few moments that stuck with us:
🔹 "Anxiety is not a policy." — Condoleezza Rice, on the need for Washington to move from hesitation to concrete action
🔹 "Closed models is a business model choice. Open models are essential for human infrastructure." — Jensen Huang, making the case for open-source AI as foundational — not optional
🔹 "What's excessive in the policy circle right now is rhetoric fueled by fear and doomerism. We need a scientific, rational understanding of AI." — Fei-Fei Li

The conversation covered everything from export controls and national security to open-source models. The agreement across the panel: the gap between political narratives and technological reality is real — and closing it matters enormously.

That's exactly why Stanford HAI created the Congressional Boot Camp on AI. Over three days, staffers from both the House and Senate engage directly with leading scholars, technologists, and policy experts to build the knowledge needed to govern this technology thoughtfully.

08/27/2026

Stanford HAI's AI Index is hiring a Research Scientist to help track, analyze, and communicate the state of AI.

The role supports the annual AI Index Report — monitoring new research, collecting and validating data, consulting outside experts, and writing technical sections of the report. It also includes maintaining and enhancing the Global AI Vibrancy Tool.

Learn more and apply:
https://careersearch.stanford.edu/hcmUI/CandidateExperience/en/sites/Stanford/job/200718/?utm_medium=jobshare&utm_source=External+Job+Share

08/26/2026

California's Delete Act requires data brokers to let consumers request deletion of their data and report how many requests they receive. A new Stanford report finds most aren't doing either.

Stanford HAI Privacy and Data Policy Fellow Jennifer King spoke to Marketplace Tech:
https://www.marketplace.org/episode/2026/08/25/californias-data-and-privacy-laws-arent-being-followed

08/25/2026

AI agents can book your travel, manage your finances, and reschedule your doctor's appointments. The more data an AI agent can access, the more useful it becomes — but also the more it can infer about your health, finances, or immigration status from data you never explicitly shared.

Our new issue brief raises two urgent questions: Will agents act in users’ best interests when developer incentives diverge, and who is liable when they don’t?

The authors argue that AI agent developers and deployers should be subject to a duty of loyalty and outline what a fiduciary framework would require from standards bodies, regulators, and Congress.

Read more: https://hai.stanford.edu/policy/designing-loyalty-ai-agents-and-conflicts-of-interest

08/25/2026

How is AI transforming empirical research?

Stanford HAI's Stanford Causal Science Center and Stanford Graduate School of Business are hosting the Empirical Methods in the Age of AI Conference on October 2-3, 2026.

The conference brings together leading academics and industry practitioners to explore how AI is reshaping data collection, causal inference, econometrics, and research workflows. The program will feature keynote talks, panels, and discussions.

Poster abstracts featuring new research, works in progress, and applied projects are welcome. Deadline: August 31.

Learn more:
DataScience.Stanford.edu/EmpiricalMethods

08/24/2026

Large language models are trained on millions of authors — but converge on a single, homogeneous voice that flattens the variation between one human writer and another.

A group of psychologists and computer scientists developed PsychAdapter to reintroduce human variation into AI-generated text by giving researchers control over personality, age, and mental health characteristics in language generation.

The result opens new possibilities for therapy training, personalized education, and social science research:

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.