Back
FAR AI Grantmaking Program
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: FAR AI
FAR AI is an AI safety research organization; this page describes their grantmaking program, relevant for researchers seeking funding or those mapping the AI safety funding landscape.
Metadata
Importance: 35/100homepage
Summary
FAR AI (Foundation for Alignment Research) runs a grantmaking program to fund AI safety research and related work. The program supports projects aligned with FAR AI's mission of reducing risks from advanced AI systems, providing financial resources to researchers and organizations working on technical and governance challenges in AI safety.
Key Points
- •FAR AI offers grants to support AI safety research projects and researchers
- •Funding targets work aligned with reducing existential and catastrophic risks from advanced AI
- •Program covers both technical AI safety and potentially governance-related work
- •Represents an organizational funding mechanism within the broader AI safety ecosystem
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| FAR AI | Organization | 76.0 |
Cached Content Preview
HTTP 200Fetched Feb 22, 20264 KB
Grantmaking
We updated our website and would love your feedback!
Events
Events
Programs
Programs
Blog
About
About
Careers Donate
Grantmaking
Funding groundbreaking research in AI safety
Sign up to our newsletter for updates
FAR.AI Grant Program
FAR.AI supports academics and independent researchers in developing innovative solutions to critical AI risks through our targeted grantmaking program. Currently, due to limited evaluation capacity, we are only able to consider researchers nominated by experts with a strong track record. We plan to launch public requests for proposals (RFPs) soon, focused on high-impact research areas. Our grantmaking is funded by a $12 million grant generously provided by Open Philanthropy.
Grants:
Failure Modes in Superhuman Systems
Florian Tramer, ETH Zurich
Broad project examining robustness across four vectors: data poisoning, consistency checks, model stealing, and prompt injection.
Comprehensive Red-Teaming Framework
Wenbo Guo, UC Santa Barbara
Building automated testing systems for LLM alignment against both training-phase threats and testing-phase threats, with a focus on developing agent-based systems that can generate adversarial prompts.
Explaining Superhuman AI Decisions
Nicholas Tomlin, UC Berkeley
Using weak-to-strong generalization to explain superhuman AI systems’ decisions, focusing on domains like chess/Go where superhuman AI already exists.
Securing Alignment
Ashwinee Panda, University of Maryland College Park
Developing methods to make alignment more secure against jailbreaks, prefilling attacks, and finetuning attacks, with approaches spanning the entire model lifecycle.
Explaining Superhuman AI Decisions
Nicholas Tomlin, UC Berkeley
Using weak-to-strong generalization to explain superhuman AI systems’ decisions, focusing on domains like chess/Go where superhuman AI already exists.
Comprehensive Red-Teaming Framework
Wenbo Guo, UC Santa Barbara
Building automated testing systems for LLM alignment against both training-phase threats and testing-phase threats, with a focus on developing agent-based systems that can generate adversarial prompts.
Securing Alignment
Ashwinee Panda, University of Maryland College Park
Developing methods to make alignment more secure against jailbreaks, prefilling attacks, and finetuning attacks, with approaches spanning the entire model lifecycle.
Failure Modes in Superhuman Systems
Florian Tramer, ETH Zurich
Broad project examining robustness across four vectors: data poisoning, consisten
... (truncated, 4 KB total)Resource ID:
f39e450eac7bbaa9 | Stable ID: ZWQwMmU4Nj