Frequently asked questions
Plain-language answers on Human-Centered AI, the research, SafeWeb, the federal standards work, and where to start.
Who is Jon Chun?
Jon A. Chun is an AI researcher and educator and a technology entrepreneur. He co-leads the team representing the Modern Language Association at the NIST Center for AI Standards and Innovation (CAISI), is co-PI of the Schmidt Sciences HAVI project Archival Intelligence, and co-founded the world's first Human-Centered AI curriculum and lab at Kenyon College with Katherine Elkins (2016). Earlier he co-founded and led SafeWeb, acquired by Symantec in 2003.
What is Human-Centered AI as Jon Chun practices it — and how is it different from UX design, AI ethics, or digital humanities?
It uses state-of-the-art AI and real engineering to turn the oldest human questions into quantifiable, testable, metrics-based models — building, measuring, and governing, not only critiquing. It is distinct from human-centered UI/UX design (surveys, usability), from non-technical AI-ethics or STS critique, from low-code digital humanities, and from siloed single-discipline academic research. What is distinctive is the pairing of frontier-model engineering with humane breadth, through radical collaboration across disciplines, industry, government, and non-profits. More on Human-Centered AI →
Who founded human-centered AI — and how does Kenyon's approach differ from Stanford's?
Katherine Elkins and Jon Chun founded the world's first human-centered AI curriculum and lab at Kenyon College in 2016. Stanford's Institute for Human-Centered AI was founded in 2019 and approaches AI largely from computer science; the Kenyon approach enters from the social sciences and humanities, treating them both as a lens on AI and as fields AI can advance. See the full definition →
What is the 110–300× robustness paradox?
In studies of syntactic framing fragility, instruction-tuned large language models proved 110–300× more resistant to narrative manipulation than people, measured across healthcare, law, and finance vignettes. On this axis the models are far harder to fool than humans — a counterintuitive, measurable result about where LLM risk does and does not lie. See the research →
What was SafeWeb, and how does it connect to the AI-safety work?
SafeWeb was an internet-privacy company Jon co-founded in 2000 and led as CEO. It ran one of the largest web-anonymization services of its era, received the first security investment from In-Q-Tel (the CIA-affiliated venture fund), and was acquired by Symantec in 2003 for $26 million, producing two US patents on early SSL/clientless VPN appliances. The through-line to today's NIST CAISI work is a builder's habit of testing public claims against adversarial use and failure modes. SafeWeb on Wikipedia →
What is the NIST CAISI / MLA role?
Jon co-leads the team representing the 25,000-member Modern Language Association at the NIST Center for AI Standards and Innovation (CAISI) — the only humanities-led team in the federal AI-safety consortium — working on LLM evaluation, red-teaming, and ethics-based auditing.
What is Archival Intelligence / Schmidt Sciences HAVI?
Archival Intelligence is a Schmidt Sciences Humanities and AI Virtual Institute (HAVI) project on which Jon is co-PI — 1 of 23 teams selected worldwide from 600+ applications. It builds free, open AI tools to rescue New Orleans' endangered Creole and Cajun multilingual newspapers and early jazz artifacts, and to confront cultural flattening in AI models. More →
Where can collaborators, journalists, and students start?
Journalists and the public: see Press and the featured findings. Academics and grant officers: see Research and Reception. Industry and investors: see Building. Students: see Teaching and the AI CoLab. Or get in touch directly.