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 favourable items would move with how many were filtered out rather than with the discourse.
30 items read as favourable
- Asymmetric Decline Rather Than Backfire: A Repost-Network Analysis of Japanese Vaccine-Shedding Misinformation on X
- Designing an AI serving harness built to last
- Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment
- Meet the Anthropic Engineer Keeping Its AI Agent Trustworthy
- Agentic Economies for Autonomous Scientific Discovery
- Using Codebooks to Detect Cybercrime Topics in Text Narratives
- Learning by Collaborating in the Age of AI: Research Mentorship for Polymathic, Workflow-Centered Science
- John Healey pledges to use budget to harness power of AI to bolster Britain’s security
- Community safety report and new protections for teens
- CriticGen: Generation-Aware Evaluation as Actionable Feedback
- Detecting AI Training in Datacentre Power
- The Moving Frontier: How the National Justice Project Is Using Digital Technology to Expand Access to Justice
- Counterfactual assessment of social media interventions by separating exposure from engagement
- Could Abdul El-Sayed’s plan for AI companies stave off catastrophe? | Jerel Ezell
- PolicyMediator: Multi-Regional Policy Learning through Mediation Agents with Evolving Skills in OPC Ecosystems
- Governed Sovereign RAG for Public Administration: Tripartite Epistemic Routing, Facet-Guided Clarification, and Deterministic Numeric Verification in Citizen Services
- Artificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities
- OpenHamoye: A Multi-Scale Open Geospatial Intelligence Framework for Decision Support Across Nigeria
- UK to invest £100m in 'world first' AI Biosecurity Lab
- The Customer Is the Builder: How AI Coding Agents Are Rewriting the Economics of Software
- ClinGuard: a governance framework for reliable, transparent, and auditable clinical coordination
- The Privacy Paradox Is a Misnomer: Data Under Structural Uncertainty
- Situating Vulnerability: An Ethical Governance Framework for Anthropomorphic AI Interaction
- ValiAssist: Scaling LLM-generated Programming Feedback Validation by Demonstration
- SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents
- L2G-SR: Learning to Guide LLM-based Symbolic Regression
- CLEAT: Learning Value-Guided Clarification for Interactive Agents
- Improving social media for democratic discourse
- The Wisdom of Artificial Deliberative Crowds
- Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models
Across the whole window
The two tables below describe every item in the last 365 days under governance. They are deliberately not narrowed by the reading filter (currently favourable), 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 |