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 · human-agency · 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 mixed or uncertain items would move with how many were filtered out rather than with the discourse.
Discourse valence, mixed or uncertain
-1
Mixed or uncertain
53 items · range -19 to +16
-100
-50
0
+50
+100
-1
Adverse readingFavourable reading
53 items read as mixed or uncertain
- EU to restrict social media and chatbots for children under 15
- A Robot Among People:From Social Imitation to the Social Becoming of Human Groups
- When Does AI Augment Work? A Workflow-Level Framework for Human-Agent Collaboration
- With a Thermomix You Lose the Ability to Cook: A Kitchen Machine Analogy for Applications of Generative AI in Education
- AI for Societal Impact
- The onward march of cognitive capitalism
- The Ineliminable Anchor: Human Judgment as a Structural Requirement of Autonomous Artificial Intelligence Systems
- Whose Ground Truth? Embracing Ambiguity in Human-Centered AI
- Guarding the Gates of Knowledge
- Can our children still prosper in the age of AI?
- Should promotion depend on how workers use AI?
- Why does America hate AI?
- Work, Wellbeing, and Choice: Empirical Lessons for AI Futures
- Anticipatory Human Oversight of Agentic AI: A Philosophical Account
- California’s Governor to Sign Landmark Online Child Safety Bills
- RADAR: Readiness for AI Discovery and Agentic Reach
- Beyond the Monolithic Interface: A Research Agenda for Plural Conversational AI
- Not All That Can Be Automated Should Be Automated: Strategic Minimalism as a Disciplined and Ethically Grounded Approach to AI Adoption
- Thick Alignment: A Social Framework for Values and AI Systems
- Ghost in the machine: how AI is haunting the search for the supernatural
- Uneven Use, Similar Trust Generative AI in Denmark and Romania
- ChatGPT’s expanding world of work
- To Manipulate or Not: Opinion Dynamics in LLM Interactions
- Anthropic turns to lawyers to help AI agents strike deals
- Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn
- Maintaining Human Verification Capacity under Automation
- Silicon Valley expects AI will kill jobs but economists are not convinced
- China has cracked down on AI relationships. Is it ahead of the game?
- My (complicated) relationship with AI: often seductive, occasionally frustrating and always demanding vigilance | Setareh Seyedghorban
- I Gave My Life Over to Meta’s A.I. Agent and Was Blown Away
- When Does the Public Become Suspicious of Bots? Demand-Side Evidence from Botometer Query Logs
- Who Decides? Agency and Legitimacy in Digital Educational Systems
- AI and Human Approaches to Mathematical Problem Solving
- The dominance of AI is not inevitable. We can choose to change things for the better | Nick Evershed
- Global maps of travel time to emergency and tertiary hospitals
- Can chatbots feel – or even dream? Meet the man leading the fight for AI rights
- Labor wants Australians to be able to opt out of online algorithms. How will it change your feed?
- Introducing the Australian Youth Safety Blueprint
- Scaffolding GenAI Use in Ethical Essay Writing: A Three-Year Experience Report in Introductory Computing
- Do job seekers value procedure in AI hiring only for error correction? Evidence from a conjoint experiment
- Beyond Cultural Knowledge: Evaluating Arabic Cultural Appropriateness of Large Language Models
- Alternative AI Philosophy: Daoism as Method for AI in Education
- From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health
- Towards a Platform for Mastering Personal Sovereignty
- Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?
- Playing Bias: Epistemic Games for Interpreting Text-to-Image Models
- An updated Model Spec for more capable and beneficial AI
- Steering LLMs Responses Towards Moral Foundations on the Norwegian MFQ-30
- Understanding Game Coaching on Gig Platforms
- Privacy Preference Controls for Agentic Systems
- Gates Foundation Pledges $1 Billion to Combat A.I. Inequality
- Artificial Intelligence and Job Design: A Dynamic Framework
- Offline Apprenticeship Learning for Human-Centric Environments
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 human-agency. They are deliberately not narrowed by the reading filter (currently mixed or uncertain), 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 |