Problem:Many critical decisions (in policy, AI safety, global health, etc.) rely on outdated or fragmented information, creating preventable risks.
Proposal:Build “Ultimate Update”—a centralized, rigorously maintained knowledge base where:
Each topic (e.g., “AI alignment,” “pandemic preparedness”) has:
A live-updated summary of the latest research/consensus.
Clear versioning to flag outdated claims (like Wikipedia + academic peer review).
Warnings for high-stakes domains where old info is dangerous (e.g., climate models, biosecurity protocols).
Governance:
Expert-curated + automated checks (e.g., ML to detect stale citations).
Funded as a public good (similar to arXiv or Our World in Data).
Why EA Should Care:
Information hazards: Prevents misallocation of resources due to obsolete data (e.g., ineffective charity interventions).
Cause-area prioritization: Could integrate with EA forums/orgs to highlight urgent updates (e.g., new AI risk papers).
Scalability: Automation + incentives could make it sustainable.
Challenges:
Avoiding information overload—how to prioritize “urgency”?
Incentivizing experts to contribute (cf. Wikipedia’s burnout issues).
Preventing misuse (e.g., weaponized misinformation).
Next Steps:
Pilot with one high-impact topic (e.g., AI safety or global health metrics).
Partner with orgs like METR, GiveWell, or FLI for domain expertise.
Ultimate Update: A Centralised Knowledge Hub to Prevent Outdated Information Risks
Problem:
Many critical decisions (in policy, AI safety, global health, etc.) rely on outdated or fragmented information, creating preventable risks.
Proposal:
Build “Ultimate Update”—a centralized, rigorously maintained knowledge base where:
Each topic (e.g., “AI alignment,” “pandemic preparedness”) has:
A live-updated summary of the latest research/consensus.
Clear versioning to flag outdated claims (like Wikipedia + academic peer review).
Warnings for high-stakes domains where old info is dangerous (e.g., climate models, biosecurity protocols).
Governance:
Expert-curated + automated checks (e.g., ML to detect stale citations).
Funded as a public good (similar to arXiv or Our World in Data).
Why EA Should Care:
Information hazards: Prevents misallocation of resources due to obsolete data (e.g., ineffective charity interventions).
Cause-area prioritization: Could integrate with EA forums/orgs to highlight urgent updates (e.g., new AI risk papers).
Scalability: Automation + incentives could make it sustainable.
Challenges:
Avoiding information overload—how to prioritize “urgency”?
Incentivizing experts to contribute (cf. Wikipedia’s burnout issues).
Preventing misuse (e.g., weaponized misinformation).
Next Steps:
Pilot with one high-impact topic (e.g., AI safety or global health metrics).
Partner with orgs like METR, GiveWell, or FLI for domain expertise.