Discourse monitor
How the monitored publications were assessed
Discourse valence across every scored item: what the sampled sources published and how independent models read it. It is not a measure of public opinion, not a measure of the state of the world, and article volume is not agreement.
Mean discourse valence per day · misinformation · last 90 days
This line is the mean of every item scored on each day. It deliberately ignores the reading filter, because a daily mean of only the adverse items would move with how many were filtered out rather than with the discourse.
Discourse valence, adverse
-45
Adverse
57 items · range -64 to -20
-100
-50
0
+50
+100
-45
Adverse readingFavourable reading
57 items read as adverse
- Mirror publisher to cut 220 editorial jobs as readers turn to AI summaries
- AI threatens to destroy so much of our culture. Our greatest loss might be our ability to listen | Shirleene Robinson
- Tool and target: US law enforcement grapple with AI-enabled terrorism and anti-tech extremism
- The Pentagon wants $30 million to build an AI-powered lie detector
- ChatGPT-using lawyer punished for citing fake testimony from made-up witnesses
- The Confidence Game: Strategic Miscalibration in Human-AI Delegation
- Black Box: The Chatbots | Happy Accident | Ep 3 – podcast
- ‘If you build something vastly smarter than you, it better be on your side’: can we stop AI from deceiving us? – podcast
- US schools and police warn about viral ‘Cat in the Hat’ trend after teens’ arrests
- Can We Still Trust Disaster Social Sensing? Empirical Evidence on Detecting AI-Generated Social Media Posts
- Reading scores plummet as ‘digital distraction’ harms students
- A.I. and Its Dangers Are Embedded In the Midterm Elections
- How A.I. Is Making It Easier Than Ever to Microtarget Voters
- Adversarial Attacks Reveal Divergent Deception Competence in Humans and Generative Language Models
- The focus on AI safety disasters ignores everyday harms
- ‘A serious failure’: CPS prosecutor cites imaginary case to high court after AI search
- “Does Meta think we can’t read?” Meta abandons sketchy commercial terms update.
- How Generative AI Redefines Personalization and Diversity in the Newsroom
- The Popular Get More Popular: Auditing Brand Bias in Generative Search
- Scare campaigns: Victoria’s major parties trade blows over AI-generated videos of leaders in election ads
- Palantir AI tech unused or little used by 29 NHS trusts said to be benefiting from it
- Language Non-Invariance in the Ideological Evaluation of Large Language Models
- AI bots flooded 2023 UK government consultation on disposable vapes ban
- AI-hallucinated citations should be treated as research misconduct
- In a rush to lure AI companies, the Australian government risks undoing a creative ecosystem that has taken decades to build | Dan Guthrie
- Opposition deepfake video sparks outrage in Spain ahead of election
- Meta Ordered to Pay $25 Billion for Deceiving Users on Privacy
- Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?
- We need to stop disinformation in UK political adverts. Here’s a chance to do so | Letter
- VoteGEO-Bench: Measuring the Impact of Generative Engine Optimization on Electoral Visibility
- Exhausted Parents Are Turning to A.I. Apps Despite the Risks
- AFL tells Sportsbet to stop using players in AI generated content
- Does the decline of book reading mark the end of rational thinking?
- AI Agents are Vulnerable to Radicalization
- Trump’s AI chatbot turns on its master
- Watching What We Eat: Information Quality and Body Image in Diet-Related YouTube Videos
- An Army of Paid Creators Is Carpeting the Web With Political Content
- Who Gets Counted? Large Language Models, Informative Refusal, and Downstream Bias in Name-Based Demographic Classification
- DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models
- Winner of the Prix Goncourt literary prize admits to using AI
- Parents and carers: how has your view on your children’s use of social media or AI changed?
- Control-Theoretic Content Moderation
- NewsBench: Expert-Grounded Evaluation of Epistemic Quality in AI News Reporting
- Can scientists reliably tell if a novel is written using AI?
- A misinformation playbook sharpens judgement without breeding cynicism
- ‘This gap opens up of doubt and scepticism about everything’: Jess and Morgs confront the politics of deepfakes
- Large language models underestimate and partly misrepresent cultural variation in everyday norms
- Giorgia Meloni seeks to trademark her voice to counter AI deepfakes
- Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations
- Social bot detection in the age of ChatGPT: Challenges and opportunities
- Breaking News Out of the Filter Bubble: Generative AI Search Diversifies Collective Attention and Raises Shared Information Consumption
- Large Language Models in the UK: Public Use, Trust, and Attitudes
- Google AI gets cagey when asked who won the 2020 elections
- Simulating Strategies for Defense Against Brand-Targeted Online Disinformation
- CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty
- New UK agency will tackle ‘information warfare’ from likes of Russia, Burnham says
- Total Simulated Survey Error: Designing and Diagnosing Survey Responses from Large Language Models
3560
discovered, all time
984
passed triage
2574
rejected at triage
3464
model assessments
710
multi-model panels
1780
distinct claims tracked
Across the whole window
The two tables below describe every item in the last 90 days under misinformation. They are deliberately not narrowed by the reading filter (currently adverse), because a mean taken only over items already selected for their score would just restate the filter.
By topic
| Topic | Branch | Items | Mean | Position |
|---|---|---|---|---|
| Existential and catastrophic risk | risk | 568 | -42 | |
| Governance and regulation | structural | 459 | -31 | |
| Concentration of power | risk | 451 | -36 | |
| Loss of control and alignment | risk | 337 | -49 | |
| Human agency and epistemics | risk | 166 | -26 | |
| Cyber capability | risk | 165 | -44 | |
| Evaluation and measurement | structural | 159 | -19 | |
| Scaling and architecture | structural | 137 | -16 | |
| AGI and timelines | structural | 132 | -29 | |
| Synthetic media and manipulation | risk | 104 | -44 | |
| Employment and displacement | risk | 62 | -31 | |
| Medicine and health | benefit | 43 | -14 | |
| Scientific acceleration | benefit | 42 | -4 | |
| Education and access to expertise | benefit | 29 | -17 | |
| Biological and chemical uplift | risk | 26 | -25 | |
| Climate and energy | structural | 20 | -33 | |
| Compute and infrastructure | structural | 18 | -8 | |
| Productivity and growth | benefit | 6 | -3 | |
| Abundance and material wellbeing | benefit | 5 | -1 | |
| Hype and disillusionment | structural | 5 | -45 |
By source
A source's mean says how the items it published were read. It is not a rating of the source.
| Source | Kind | Tier | Items | Mean |
|---|---|---|---|---|
| The Guardian: Artificial Intelligence | news | 1 | 241 | -52 |
| arXiv cs.CY (Computers and Society) | academic | 1 | 217 | -24 |
| Financial Times: Artificial Intelligence | news | 1 | 112 | -40 |
| arXiv cs.AI | academic | 1 | 99 | -15 |
| The New York Times: Technology | news | 1 | 60 | -36 |
| Ars Technica: AI | news | 1 | 59 | -51 |
| MIT Technology Review: AI | news | 1 | 34 | -42 |
| Nature | academic | 1 | 31 | -25 |
| BBC News: Technology | news | 1 | 29 | -35 |
| OpenAI | lab | 1 | 24 | -8 |
| arXiv cs.LG (Machine Learning) | academic | 1 | 24 | -16 |
| IEEE Spectrum: AI | news | 1 | 19 | -19 |
| The Economist: Science and Technology | news | 1 | 10 | -37 |
| CSET, Georgetown | government | 1 | 9 | -37 |
| Microsoft Research | lab | 1 | 8 | 0 |
| Google DeepMind | lab | 1 | 6 | -4 |
| Google AI Blog | lab | 1 | 2 | -9 |