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.
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.
31 items read as mixed or uncertain
- The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
- DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution
- Developing Competency in AI Ethics: A training framework
- How “reverse game theory” could keep AI agents in check
- Evaluating Model Capabilities After Benchmark Saturation
- Whose Ground Truth? Embracing Ambiguity in Human-Centered AI
- SlackBench: Benchmarking Agents on Collaborative Projects Grounded in Real Code Repositories
- AI Risk Ontologies for the EU AI Act Compliance: AIRO, VAIR, and their Integration with DPV
- From Regulation to Engineering: Organizational Privacy Climate as Mediator
- World Bank says ‘small AI’ could transform developing countries
- Distilling Expert Judgment at Scale
- Trust Responsive Runtime Authorization for Agentic AI with Empirical Characterization of Policy Behavior and Parameter Sensitivity
- A Feasibility Condition for Confidence-Gated Tiered LLM Inference
- Don't Pick a Winner: Knowledge Conflicts Need a Prior, Not a Rule
- Uneven Use, Similar Trust Generative AI in Denmark and Romania
- AOS-Bench: Can LLM Agents Manage Dynamic Workloads?
- When Reconstruction Is Not Enough: Doubly Robust Counterfactual and Interventional Mediation Analysis with Learned Multimodal Representations
- Peer review should constrain evaluative authority
- When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability
- “We were looking at it wrong”: The explosive claim that’s upended the AI world
- From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring
- Shaping & Monitoring Models through their Internal Representations
- One essential ingredient for AI safety? Democracy | Matthew Botvinick
- ‘Control and sovereignty’ to shape UK approach to AI
- HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation
- Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI
- Synthesizing symbolic models of human and machine reasoning at scale
- OpenFrontiers: A Collection of Open Research Problems and a Benchmark for Scientific Rediscovery
- The EU AI Act Beyond Europe: A Practical Readiness Framework for Non-EU Organisations. Comparative Evidence from Four African Countries
- Anthropic gives outside AI watchdog “employee-like” access
- AI-Native Academia Should Build a Community Verification Layer
Across the whole window
The two tables below describe every item in the last 30 days under evaluation. 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 |