Work that others build on
How research Jon Chun has co-authored — on language-model evaluation, AI regulation, and computational narrative — is taken up across fields. Named citers and structural adoption, not citation counts.
The work is now part of the reference layer other researchers build on. Its three-regime map is standard shorthand for how the EU, China, and the United States diverge; its ethics-based audit is a reference method at the AAAI/ACM Conference on AI, Ethics, and Society; and its narrative methods are a scaffold other teams build on. Uptake runs from Terence Tao to PNAS Nexus and into chemistry, medicine, and the technical machine-learning literature. Most of this work is co-authored with Katherine Elkins, whose reception page documents the same corpus in further detail.
Evaluating and governing frontier models
Informed AI Regulation: Comparing the Ethical Frameworks of Leading LLM Chatbots
The first ethics-based audit of moral reasoning in deployed LLMs — the paper that operationalized ethical evaluation as a systematic audit — and now the anchor of a strand of AI-safety evaluation work. Liu and colleagues' INVP framework (COLING 2025) directly adopts its confidence-scoring methodology for evaluating LLM value priorities; Sowmya and Vasudeva (IEEE Access, 2026) replicate the eight-model audit design outright. At AIES, Jain, Calacci and Wilson cite its central finding that LLM ethical reasoning shows a clear bias toward societal and cultural norms, and the Edinburgh LLM Ethics Whitepaper names it a representative methodology for probing ethical values through prompting.
Comparative Global AI Regulation: Policy Perspectives from the EU, China, and the US
The first systematic EU–China–US regulatory comparison after the EU AI Act's passage. Its three-regime map — horizontal risk-based, market-driven, state-led vertical — has become standard shorthand in the citing literature. Floridi and Ascani cite it in Minds and Machines on AI for legislative innovation in the Italian Parliament; Olugbade engages it across a Global Public Policy and Governance argument for a global governance mechanism; and its regulatory-sandbox recommendation is adopted in Lu and Tie's ASEAN–EU comparison. Uptake runs through Communications of the ACM, JCMS on EU digital sovereignty, Information Fusion on trustworthy AI in healthcare, and FAccT red-teaming of the AI Act. Vaughan C. Turekian draws on the three-regime map in PNAS Nexus, citing it as evidence that AI governance is fragmenting along national lines.
Near to Mid-term Risks and Opportunities of Open-Source Generative AI
This position paper set the benefit–risk terms of the open-model governance debate. Taeihagh's "Governance of Generative AI" (Policy and Society, 2025) carries its access-spectrum analysis; Liesenfeld and Dingemanse build on it in their FAccT "open-washing" critique of the EU AI Act; and Casper, O'Brien, Longpre and colleagues cite it in their TMLR consensus paper on open-weight model risk — authored by safety researchers across MIT CSAIL, Stanford, Mila, Carnegie Mellon, Princeton, the UK AI Security Institute, and Hugging Face.
Measuring the shape of stories
Can GPT-3 Pass a Writer's Turing Test?
The first writer's Turing test of a large language model — published within months of GPT-3's release, and cited within the year by Floridi and Chiriatti for the claim that "GPT-3 writes better than many people." The paper now sits in the reference layer of the LLM literature, and both sides of the capability debate quote it. It appears in PNAS (Mei and colleagues' behavioral Turing test), Science Advances (Spitale and colleagues on GPT-3 disinformation), the most-cited paper on LLMs in education (Kasneci and colleagues), and NeurIPS, EMNLP and NAACL work on impersonation and narrative bias. In literary studies, N. Katherine Hayles engages it in Bacteria to AI (2025) and Martin Paul Eve cites it across two books.
The Shapes of Stories: Sentiment Analysis for Narrative
The first methodology for sentiment analysis of narrative: an ensemble approach benchmarking more than three dozen models and showing how smoothing, model selection, and interpretation determine what an emotional arc reveals. The sciences now test its claims at scale: Knight and Rocklage's Science Advances study of narrative reversals and He, Breithaupt, Kübler and Hills's 25,728-retelling study in Scientific Reports both ground their designs in emotional-arc analysis. In NLP it is core infrastructure for leading computational-narrative groups, and the method has traveled to customer-journey research at Harvard Business School, game-feel design in IEEE Transactions on Games, and sustainability science.
Can Sentiment Analysis Reveal Structure in a Plotless Novel? (and "middle reading")
This paper introduced "middle reading" and was the first to test whether sentiment methods survive nonlinear narrative — the question computational literary studies still benchmarks against. The Aarhus group cites "Elkins and Chun 2019" across at least six papers on Hemingway, Danish literature, and literary-quality prediction as the standing reference for that question; Sui, Hamilton and colleagues call nonlinearity "a hard problem for narratology, by both computational (Elkins and Chun, 2019) and traditional approaches."
eXplainable AI with GPT-4 for Story Analysis
The journal's special issue on Reproducibility and Explainability names the paper as proposing "an advanced XAI approach and workflow for LLM-based research." Cugurullo and Xu carry it into political theory in Policy and Society, citing it as the authority on LLMs' opaque epistemology; surveys in Discover Applied Sciences and IEEE Access catalogue it as the exemplar of GPT-4 story analysis.
A new curriculum
The Crisis of Artificial Intelligence: A New Digital Humanities Curriculum for Human-Centred AI
One of the first human-centered AI curricula in the digital humanities, and the channel through which the work entered education policy, mathematics, and global health. Terence Tao (with Tanya Klowden) draws on it for the observation that the tasks AI automates "did not require an understanding of more philosophical aspects of a profession, such as the nature of knowledge, beauty, meaning." UNESCO's Prospects quotes its diagnosis of the "AI crisis" in the digital humanities; the Frontiers in Education review of AI and Education 4.0 builds on the curriculum framework; and engagement runs into chemistry (Journal of Chemical Education) and medical education (Informatics in Medicine Unlocked).
See the full research record → · Grants & honors → · Google Scholar →
Who builds on the work, and where
The work bridges communities that rarely cite one another — supplying methods, frameworks, and reference points that each field takes up in its own terms.
The fields that take up the work
Where the work is read
Countries where indexed research cites the work, drawn from structured affiliation data. A coverage view: books, chapters, and non-English venues are under-represented, so the actual reach is wider than shown. Hover a marker for the country name.