The Peril and Promise of AI for Journalism
Bibliographic record
Abstract
Journalists around the world are responding to the dramatic rise of artificial intelligence (AI), and particularly generative AI. Many newsrooms are experimenting with these tools and identifying new opportunities. At the same time, many journalists and commentators see AI as the biggest threat to journalism in decades.This report draws on insights from researchers and journalists to address three major issues: 1) How generative AI can make disinformation campaigns faster, more targeted, and more persuasive. 2) How newsrooms’ adoption of AI tools can lead to inaccuracies and other risks. 3) How AI may threaten the viability of professional journalism, including through automation and content generation that replaces human journalists.The report examines how journalists are improving investigative practices to expose disinformation campaigns, experimenting with AI tools to make their own work more efficient, and developing ethical guidelines and labour protections to defend professional journalism. It argues for the news industry and policymakers to undertake further actions to safeguard professional journalism and its contributions to democratic societies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.097 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.035 | 0.039 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".