Corrigibility Transformation: Constructing Goals That Accept Updates
Quick summary
arXiv:2510.15395v2 Announce Type: replace Abstract: An AI agent will learn a desired goal more effectively if it does not resist the training process, but many partially learned goals incentivize an AI to avoid further goal updates. We would like goals to be corrigible, meaning they allow changes requested through designated channels, so that we can confidently correct errors and shut down the AI if necessary. Despite this being a crucial safety property, the existing literature does not specify goals that are both corrigible and competitive with alternatives. We introduce a transformation tha
Key takeaways
- arXiv:2510.15395v2 Announce Type: replace Abstract: An AI agent will learn a desired goal more effectively if it does not resist the training process, but many partially learned goals incentivize an AI to avoid further goal updates.
- We would like goals to be corrigible, meaning they allow changes requested through designated channels, so that we can confidently correct errors and shut down the AI if necessary.
- Despite this being a crucial safety property, the existing literature does not specify goals that are both corrigible and competitive with alternatives.
Why it matters
“Corrigibility Transformation: Constructing Goals That Accept Updates” shows why AI risk cannot be reduced to answer accuracy. Access controls, logging, human approval and incident response need to be designed into the workflow from the start.
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