Jon Chun came to Kenyon from Silicon Valley, where he co-founded SafeWeb, the world's largest privacy and anonymity platform, backed by In-Q-Tel. In 2016, six years before ChatGPT brought generative AI to public attention, he and Katherine Elkins founded the world's first human-centered AI curriculum and the AI CoLab. Their 2023 article offered the first peer-reviewed account of what a human-centered AI curriculum looks like. Chun created SentimentArcs, an open-source method of mapping the emotional architecture of stories that researchers worldwide have adopted, and his current work centers on explainable AI, ethics-based audits of language models, and global AI regulation.

Alongside mentoring more than 400 student research projects, downloaded over 107,000 times in 198 countries, Chun co-leads the Modern Language Association team at the U.S. Safety Institute Consortium (NIST CAISI) and is co-PI of the Schmidt Sciences Archival Intelligence project.

Areas of Expertise

Research in human-centered AI, AI agents, affective computing, narrative, security/privacy, generative AI benchmarking, eXplainable AI (XAI), AI fairness bias transparency explainability (FATE), ethical and compliance auditing, and AI policy/regulation. Domain expertise in HealthTech, FinTech, InsurTech, Security, and Entrepreneurship.

Education

1995 — Master of Science from University of Texas at Austin

1989 — Bachelor of Science from Univ. of California Berkeley

Courses Recently Taught

This course explores artificial intelligence through both technical implementation and humanistic inquiry. Building on the programming foundations from IPHS 200, students learn to build and critically evaluate AI systems, from classical machine-learning approaches to cutting-edge deep neural networks and large language models. Through hands-on projects, students create AI systems that generate music, analyze text, classify images and more. The course pairs technical training with readings from philosophy, ethics and critical theory to examine fundamental questions about creativity, intelligence, and what it means to be human in an age of artificial minds. The course emphasizes both technical competency and critical thinking, preparing students to be thoughtful practitioners and critics in our AI-driven future. Prerequisite: COMP 118, IPHS 200 or IPHS 391 (fall 2025).

This upper-division course provides an in-depth, hands-on exploration of advanced generative AI concepts with interdisciplinary applications. Students engage with a progressive curriculum covering large language models (LLMs), AI information systems, and autonomous multi-agent systems. The course emphasizes four practical project areas: (1) API-based chatbot development with structured function calling, (2) embeddings and model explainability, (3) Retrieval- Augmented Generation (RAG) for knowledge-enhanced applications, and (4) multi-agent simulations for modeling complex scenarios. Students complete four mini-projects and one original interdisciplinary research project, building a professional portfolio demonstrating technical proficiency and creative problem-solving. \n\nRequired Subscriptions: Students will be required to purchase subscriptions to leading AI model services (such as OpenAI, Anthropic, or Google) for approximately four months. Some services offer free access for students with .edu email accounts. Total subscription costs shall not exceed $150 for the semester without prior CPC approval. Students experiencing financial hardship should contact the instructor to discuss accommodation options. Prerequisite: COMP 118 or IPHS 200.

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