TLDR: CAIS is distributing $250,000 in prizes for benchmarks that empirically assess AI safety. This project is supported by Schmidt Sciences, submissions are open until February 25th, 2025. Winners will be announced April 25th, 2025.
To view additional info about the competition, including submission guidelines and FAQs, visit https://www.mlsafety.org/safebench
If you are interested in receiving updates about SafeBench, feel free to sign up on the homepage using the link above.
About the Competition:
The Center for AI Safety is offering prizes for the best benchmarks across the following four categories:
- Robustness: designing systems to be reliable in the face of adversaries and highly unusual situations.
- Monitoring: detect malicious use, monitor predictions, and discover unexpected model functionality Alignment: building models that represent and safely optimize difficult-to-specify human values.
- Safety Applications: using ML to address broader risks related to how ML systems are handled.
Judges:
- Zico Kolter, Carnegie Mellon
- Mark Greaves, AI2050
- Bo Li, University of Chicago
- Dan Hendrycks, Center for AI Safety
Timeline:
Mar 25, 2024: Competition Opens
Feb 25, 2025: Submission
Deadline Apr 25, 2025: Winners Announced
Competition Details:
Prizes: There will be three prizes worth $50,000 and five prizes worth $20,000.
Eligibility: Benchmarks released prior to the competition launch are ineligible for prize consideration. Benchmarks released after competition launch are eligible. More details about prize eligibility can be found in our terms and conditions.
Evaluation criteria: Benchmarks will be assessed according to this evaluation criteria. In order to encourage progress in safety, without also encouraging general advances in capabilities, benchmarks must clearly delineate safety and capabilities.
Submission Format: If you have already written a paper on your benchmark, submit that (as long as it was published after the SafeBench launch date of March 25th, 2024). Otherwise, you may submit a thorough write-up of your benchmark, including source code. An example of such a write-up can be found in this document.
Dataset Policy: By default, we will require the code and dataset for all submissions to be publicly available on Github. However, if the submission deals with a dangerous capability, we will review whether to publicly release the dataset on a case-by-case basis.
If you are interested in receiving updates about SafeBench, feel free to sign up on the homepage: https://www.mlsafety.org/safebench
