This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
AI Governance Hackathon
Accepted at the 
AI Governance Hackathon
 research sprint on 
July 21, 2023

Data Taxation

In this paper, we propose a tax that affects the training of any model of sufficient size, and a concrete formula for implementing it. We explain why we think our framework is robust, future-proof and applicable in practice. We also give some concrete examples of how the formula would apply to current models, and demonstrate that it would heavily disincentivize work on ML models that could develop AGI-like capabilities, but not other useful narrow AI work that does not pose existential risks.

Joshua Sammet, Per Ivar Friborg, William Wale
4th place
3rd place
2nd place
1st place
 by peer review