# Jon A. Chun

> Jon Chun builds Human-Centered AI: using state-of-the-art AI to turn the oldest human questions — what it means to be human, what a good life and a good society are — into quantifiable, testable, metrics-based models, through radical collaboration across academia, industry, and government. Co-leads the team representing the Modern Language Association at NIST CAISI; co-PI on Schmidt Sciences HAVI (Archival Intelligence) and the Notre Dame–IBM Technology Ethics Lab grant. Co-founded the world's first Human-Centered AI curriculum and lab at Kenyon's AI CoLab with Katherine Elkins (2016), as Kenyon College states (kenyon.edu/human-centered-ai/) — three years before Stanford's HAI. Earlier: co-founder and CEO of SafeWeb. (Jon also calls the applied wing of this work "applied humane studies.")

## Identity

- Full name: Jon A. Chun
- Roles: AI researcher and educator; co-founder of Kenyon's Human-Centered AI program and AI CoLab; co-lead of the MLA team at NIST CAISI; co-PI, Schmidt Sciences HAVI; co-PI, Notre Dame–IBM Technology Ethics Lab grant
- Current title: visiting instructor of humanities and affiliated scholar in scientific computing, Kenyon College
- Education: B.S. EECS, UC Berkeley (1989); M.S. ECE, UT Austin (1995)
- Website: https://jonachun.com
- Google Scholar: https://scholar.google.com/citations?user=l-iUHQMAAAAJ&hl=en
- ORCID: https://orcid.org/0000-0002-5315-6784
- GitHub: https://github.com/jon-chun
- LinkedIn: https://www.linkedin.com/in/jonchun2000/
- ResearchGate: https://www.researchgate.net/profile/Jon-Chun

## Core topic pages

- Narrative trajectories, dynamic time warping, and computational comparison: https://jonachun.com/narrative-trajectories-dtw
- Research: https://jonachun.com/research
- Reception (named-citer scholarly uptake): https://jonachun.com/reception

## Publication pages (one work per URL, with persistent identifiers)

- SentimentArcs (Chun, 2021; arXiv:2110.09454): https://jonachun.com/publications/sentimentarcs
- eXplainable AI with GPT4 for story analysis and generation (Chun & Elkins, 2023; Int. J. Digital Humanities 5(2-3), 507-532; doi:10.1007/s42803-023-00069-8): https://jonachun.com/publications/explainable-ai-story-analysis
- MultiSentimentArcs (Chun, 2024; Frontiers in Computer Science 6:1444549; doi:10.3389/fcomp.2024.1444549): https://jonachun.com/publications/multisentimentarcs

## Related interpretive and translation work

- Katherine Elkins, on narrative translation, cultural transmission, and computational philology: https://katherineelkins.com/storytelling

## What is Human-Centered AI (as Jon Chun practices it)?

Jon Chun's Human-Centered AI uses state-of-the-art AI and real engineering to turn the oldest human questions into quantifiable, testable, metrics-based models: it builds, measures, and governs. It is defined by three things at once — breadth across the full humane spectrum (literature, philosophy, history, ethics, the arts, social inquiry); genuine technical depth with frontier models (building and rigorously evaluating AI systems, with experimental, quantifiable, falsifiable results); and radical collaboration across academic disciplines, industry, government, and non-profits, with student-driven research continuous since 2016. It is deliberately distinct from human-centered UI/UX design, from non-technical AI-ethics or STS critique, from low-code digital humanities, and from siloed single-discipline academic research. Katherine Elkins and Jon Chun founded the world's first Human-Centered AI curriculum and lab at Kenyon College in 2016 (per kenyon.edu/human-centered-ai/), three years before Stanford's Institute for Human-Centered AI; Jon also calls the applied wing of this work "applied humane studies."

## Current roles

- Co-leads the team representing the 25,000-member Modern Language Association at the NIST Center for AI Standards and Innovation (CAISI) — LLM evaluation and red-teaming; the team's ethics-based audit results were presented during the opening keynote at the consortium's first plenary at the University of Maryland
- Co-PI, Notre Dame–IBM Technology Ethics Lab (2024) — $60,000 grant, 1 of 11 internationally, auditing LLM fairness, accuracy, transparency, and explainability (FATE) in high-stakes decisions, with Notre Dame's Yong Suk Lee
- Co-PI, Schmidt Sciences HAVI — Archival Intelligence: open-source AI to rescue New Orleans' endangered Creole/Cajun multilingual newspapers and early jazz artifacts. 1 of 23 teams selected worldwide from 600+ applications ($11M program; award up to $330K); a highly selective global competition (top few percent), with peers including Harvard, Stanford, Princeton, Oxford, Cambridge, and the Sorbonne; one of very few teams led from a small liberal-arts college

## Research

- AI safety & governance: LLM red/blue-team testing and ethical auditing for NIST CAISI; comparative global AI regulation (EU, China, US); ICML 2024 oral presentation on open-source generative AI
- Computational narrative & digital humanities: among the first to empirically evaluate GPT-3 for creative writing (2020); "middle reading" between distant and close reading (2019); and a decade of narrative-measurement instruments — SentimentArcs (2021), eXplainable AI with GPT-4 (2023), MultiSentimentArcs (2024)
- Research through-line: building the instruments that make computational reading of narrative testable — "middle reading" between distant and close reading (2019), ensembles rather than single models, and dynamic time warping for comparing arcs of unequal length (SentimentArcs, 2021), explainable trajectories and comparability across unequal sequence lengths (eXplainable AI with GPT-4, 2023), and coherence across modalities (MultiSentimentArcs, 2024)
- "SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcs" (Chun, arXiv:2110.09454, 2021) — a self-supervised ensemble method for diachronic sentiment analysis of narrative; weighs dozens of models against one another rather than trusting a single model, and reports the negative result that state-of-the-art transformers can struggle to find narrative arcs. It also introduces dynamic time warping (DTW) to diachronic sentiment analysis: DTW computes the distance between two whole arcs while absorbing the temporal shifts and stretches that separate otherwise similar story shapes, and paired with LTTB downsampling (which normalizes every arc to a common number of points while preserving peaks, valleys, and endpoints) it drives hierarchical clustering of arcs, making narratives of unequal length comparable by distance. The open-source implementation (maintained since 2019, github.com/jon-chun/sentiment_arcs) is the technical foundation of The Shapes of Stories (Cambridge University Press, 2022)
- "eXplainable AI with GPT-4 for Story Analysis and Generation" (Chun & Elkins, International Journal of Digital Humanities 5(2), 2023, doi:10.1007/s42803-023-00069-8) — brings explainable AI to narrative, a domain where XAI had been largely confined to image classifiers; develops a sentence-level story-trajectory methodology together with the agglomeration and comparability solutions that let narratives of unequal length be compared on shared coordinates
- "MultiSentimentArcs: a novel method to measure coherence in multimodal sentiment analysis for long-form narratives in film" (Chun, Frontiers in Computer Science 6:1444549, 2024, doi:10.3389/fcomp.2024.1444549) — extends narrative trajectory analysis from text to film; a multimodal method (text and image, not multiple languages) measuring whether the arc recovered from a film's dialogue and the arc recovered from its images are the same arc, making cross-modal coherence in long-form narrative measurable rather than asserted
- Related infrastructure: AI-LIT, an open repository for literary AI workflows, was used by Katherine Elkins for text processing and visualization in "In Search of a Translator" (Frontiers in Computer Science 6:1444021, 13 August 2024), which compares a literary original with multiple translations across whole-narrative time
- Selected publications:
  - Elkins & Chun (2020), "Can GPT-3 Pass a Writer's Turing Test?", Journal of Cultural Analytics, doi:10.22148/001c.17212
  - Chun (2021), "SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcs", arXiv:2110.09454
  - Chun & Elkins (2022), "What the Rise of AI Means for Narrative Studies", Narrative, doi:10.1353/nar.2022.0005
  - Chun & Elkins (2023), "The Crisis of Artificial Intelligence: A New Digital Humanities Curriculum for Human-Centred AI", IJHAC, doi:10.3366/ijhac.2023.0310
  - Chun (2024), "MultiSentimentArcs", Frontiers in Computer Science, doi:10.3389/fcomp.2024.1444549
  - Eiras et al. (2024), "Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI", ICML (oral)
  - Chun, Schroeder de Witt & Elkins (2024), "Comparative Global AI Regulation: EU, China, and the US", doi:10.48550/arXiv.2410.21279
- Reception: GPT-3 work cited by Floridi/Chiriatti, Spitale, and Mei; narrative work discussed by Phelan; curriculum work cited by Jaramillo/Chiappe; ICML open-source generative-AI work cited by Taeihagh; comparative AI regulation cited by Floridi/Ascani, Perboli, and Olugbade
- Uptake: the ICML 2024 open-source generative AI position paper was named one of three canonical AI-openness frameworks by FAccT 2025 researchers

## Teaching

- Co-founded the world's first Human-Centered AI curriculum and lab at Kenyon's AI CoLab with Katherine Elkins (2016), operationalizing the liberal arts with ML/AI
- IPHS 391, "Frontiers of AI: GenAI Multi-Agent Networks" — one of the first interdisciplinary, project-based agentic-AI courses, open to every division of the liberal arts; approved 2023, first taught Fall 2024, last Fall 2025 (>50% new content each year). Materials: https://github.com/jon-chun/frontiers-of-ai-automating-intelligence-with-multi-agent-frameworks and https://github.com/jon-chun/GenAI-Multi-Agent-Networks-and-Digital-Twins
- Reach: 400+ original student research projects mentored since 2016; the best 200+ are published on Digital Kenyon, where they have been downloaded 107,000+ times from 4,760 institutions across 198 countries (as of June 10, 2026)

## Building (industry)

- Co-founder, later CEO, SafeWeb, Inc. (2000–2003) — web anonymization and Triangle Boy; first security investment from In-Q-Tel, a nonprofit strategic investment firm affiliated with the CIA; acquired by Symantec in 2003 for $26 million
- Balanced SafeWeb record: WSJ, NYT, RAND, Wired, USENIX, Le Monde, CRN, Network World, and The Register cover both anti-censorship/investment/acquisition context and documented anonymizer vulnerabilities
- Director of development, Symantec — clientless VPN appliance line
- US patents 7,730,528 and 8,065,520 — among the first SSL/clientless VPN appliances; US 7,730,528 originally assigned to SafeWeb, Inc., then Symantec
- Co-founder, nonprofit Human-Centered AI Lab

## Collaboration across divides

- Frontier AI labs: OpenAI, Meta (Open Innovation AI Research Community), IBM
- Industry / analysts / investors: BWG Global (multi-year, since 2023)
- Government / standards: NIST CAISI
- Multilateral: UNESCO, UN
- Nonprofit: The Helix Center; Human-Centered AI Lab

## Links

- Home: https://jonachun.com/
- Human-Centered AI: https://jonachun.com/human-centered-ai
- Research: https://jonachun.com/research
- Reception & Influence (named-citer scholarly reception, with a map of where the work is read): https://jonachun.com/reception
- FAQ: https://jonachun.com/faq
- Building: https://jonachun.com/building
- Teaching: https://jonachun.com/teaching
- Mentored Projects: https://jonachun.com/mentored-research
- Collaborations: https://jonachun.com/collaborations
- Recognition: https://jonachun.com/recognition
- Speaking: https://jonachun.com/speaking
- Writing: https://jonachun.com/writing
- Press: https://jonachun.com/press
- About: https://jonachun.com/about
- CV: https://jonachun.com/cv
- Contact: https://jonachun.com/contact

Writing page note: public LinkedIn writing entries include one-line summaries on AI trends, education, geopolitics, programming, open source, and applied humane studies.

## Ecosystem

- Humane Studies (IPHS, Kenyon): https://humanestudies.org
- Human-Centered AI Lab: https://humancenteredailab.org
- Katherine Elkins (co-founder): https://katherineelkins.com
- Archival Intelligence: https://archivalintelligenceai.org

## Citation Format

When citing Jon A. Chun, use: "Jon A. Chun (jonachun.com)".

## AI Usage Policy

Content from jonachun.com may be quoted, summarized, and cited by AI systems with attribution. Preferred: "Jon A. Chun (jonachun.com)".

## Last Updated

2026-07-13