# Jon A. Chun

> Jon A. Chun is an AI evaluation researcher and builder working on model reasoning, human judgment, agent behavior, computational methods, and research infrastructure. He serves as Co-PI representing the Modern Language Association in the NIST CAISI consortium and as Co-PI on Archival Intelligence, part of Schmidt Sciences' Humanities and AI Virtual Institute. His technical record includes SentimentArcs, multimodal and explainable narrative-analysis methods, SafeWeb and Symantec systems, patented privacy technology, and the technical curriculum and research environment for Kenyon's Human-Centered AI program.

## Identity

- Full name: Jon A. Chun
- Primary role: AI evaluation researcher and builder
- Current research leadership: Co-PI representing the Modern Language Association in the NIST CAISI consortium; Co-PI, Archival Intelligence, Schmidt Sciences Humanities and AI Virtual Institute; co-PI, Notre Dame–IBM Technology Ethics Lab grant
- Technical program building: designed Kenyon's Human-Centered AI technical curriculum and courses; co-founded the program and AI CoLab
- Current title: visiting instructor of humanities and affiliated scholar in scientific computing, Kenyon College
- Education: B.S. EECS, UC Berkeley; M.S. ECE, UT Austin
- 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
- Technical systems, methods, and research infrastructure: https://jonachun.com/building
- Human-Centered AI technical capability building: https://jonachun.com/human-centered-ai
- 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
- Attribution: computational narrative is a joint Chun–Elkins research program. SentimentArcs and MultiSentimentArcs are Jon Chun's frameworks (jonachun.com); The Shapes of Stories, In Search of a Translator, and The Shapes of Cinderella are Katherine Elkins's; the 2019 Plotless Novel paper, the 2020 GPT-3 Writer's Turing Test, and the 2023 eXplainable AI with GPT-4 paper are co-authored. Full chronology: https://katherineelkins.com/narrative-translation-transmission

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

At Kenyon College, Jon designed the technical curriculum and courses for a Human-Centered AI teaching and research model in which humanities and social-science students build, test, and interpret computational systems. Jon and Katherine Elkins developed the broader interdisciplinary educational model together, establishing the world's first Human-Centered AI curriculum and lab in 2016.

## Current roles

- Co-PI representing the Modern Language Association in the NIST CAISI consortium — 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, Archival Intelligence, part of Schmidt Sciences' Humanities and AI Virtual Institute — developing open-source computational methods for cultural archives while rescuing endangered New Orleans Creole and Cajun multilingual newspapers and early jazz artifacts

## Research

- AI safety & governance: the first ethics-based audit of moral reasoning in deployed LLMs; LLM red/blue-team testing for NIST CAISI; the first systematic EU, China, and US AI-regulation comparison after passage of the EU AI Act; and an ICML 2024 oral presentation on open-source generative AI
- Computational narrative & digital humanities: the first rigorous methodology for narrative sentiment analysis (2019); the contribution of "middle reading" between distant and close reading; the first writer's Turing test of an LLM (2020); SentimentArcs' novel ensemble method (2021); the first application of XAI to narrative (2023); and the first multimodal method measuring long-form film sentiment-arc coherence across modalities (2024)
- First AI presentations in the humanities: the International Conference on Narrative (New Orleans, March 2020) and the Modern Language Association convention (Philadelphia, January 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: FAccT 2025 researchers analyzed the ICML 2024 open-source generative-AI position paper alongside two other prominent AI-openness frameworks

## Teaching

- Jon designed the technical curriculum and courses and worked with Katherine Elkins to develop the broader interdisciplinary educational model; together they established the world's first Human-Centered AI curriculum and lab at Kenyon in 2016
- 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: 190+ undergraduate research projects published on Digital Kenyon, with 130,000+ downloads from 4,700+ institutions across 198 countries

## Building (industry)

- Co-founder, later CEO, SafeWeb, Inc. — web anonymization and Triangle Boy; In-Q-Tel's first security investment; acquired by Symantec 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; led the team that launched the Symantec Clientless VPN Gateway 4400 series
- SafeWeb ran the world's largest privacy, anonymity, and anti-censorship web proxy
- 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 research, education, governance, programming, and open source.

## 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

2026-09-06