"Corrigibility" is usually defined as the property of AIs who don't resist modifications by their designers. Why would we want to perform such modifications? Mainly it's because we made errors in the initial implementation, and in particular the initial implementation is not aligned. But, this leads to a paradox: if we assume our initial implementation to be flawed in a way that destroys alignment, why wouldn't it also be flawed in a way that destroys corrigibility?

In order to stop passing the recursive buck, we must assume some dimensions along which our initial implementation is not allowed to be flawed. Therefore, corrigibility is only a well-posed notion in the context of a particular such assumption. Seen through this lens, the Hippocratic principle becomes a particular crystallization of corrigibility. Specifically, the Hippocratic principle assumes the agent has access to some reliable information about the user's policy and preferences (be it through timelines, revealed preferences or anything else).

Importantly, this information can be incomplete, which can motivate altering the agent along the way. And, the agent will not resist this alteration! Indeed, resisting the alteration is ruled out unless the AI can conclude with high confidence (and not just in expectation) that such resistance is harmless. Since we assumed the information is reliable, and the alteration is beneficial, the AI cannot reach such a conclusion.

For example, consider an HDTL agent getting upgraded to "Hippocratic CIRL" (assuming some sophisticated model of relationship between human behavior and human preferences). In order to resist the modification, the agent would need a resistance strategy that (i) doesn't deviate too much from the human baseline and (ii) ends with the user submitting a favorable report. Such a strategy is quite unlikely to exist.