legit-dynamics-learning
frame · drafted
Question
Under what conditions does an AI learning the humans' concept of legitimacy learn enough of it to avoid wireheading and manipulation, and can legitimacy, once lost, be repaired?
Owners: @anson (GitHub A-M-Berns), @abram (GitHub abramdemski)
Path: the-program › legitimacy › legit-dynamics-learning
Roots (the frame's goals)
plan
A formal model of complete feedback links legitimacy with the reflection principle and the endorsement framework and shows corrigibility to be rational.
risk-story
Manipulation of the deliberation process, lock-in effects, path dependence and mesa-optimizers that steer deliberation can move a system out of the basin of legitimate deliberation.
shared-claim
Under stated conditions an AI learning the humans' legitimacy concept learns enough of it to avoid wireheading and manipulating the humans.
The accumulated probability that the process's legitimacy is lost along a finite trajectory is bounded by a quantity the theory computes.
Legitimacy, once lost, can be repaired under stated circumstances.
Illegitimacy is learned mostly from others' cases and by extrapolation from known phenomena, rather than through the agent's own temporary illegitimacy.
Partial corruption of the process is reversible by a mechanism the model states, and total but reversed corruption is modelled by the same mechanism.
Every harmful self-fulfilling prophecy is corruption in the map-territory sense, so that a legitimate predictor is by definition one that does not produce them.
Legitimacy and corruption are definable as negations of each other: worlds with corrupt feedback are exactly the worlds that are not truth-tracking.
Counterfactual oracles are not a special case of the legitimacy framework, while actual oracles are recovered in the all-legitimate case, where Tower is recovered.
A process-based definition says what makes deliberation legitimate rather than illegitimate, without circularity.
Legitimacy relates to endorsement as goodness relates to utility: endorsement is the operational attitude and legitimacy the ideal it answers to.
A formal structure, a dynamical system or the limit of a sequence, makes precise the assumption that humans converge to an adequate articulation of their values under legitimate deliberation and that the AI predicts that limit.
Small errors in tracking legitimate deliberation self-correct rather than compound, under stated stability properties.
Legitimate deliberation has its own fixed-point selection problem, and the theory says which reflective equilibrium it selects.
Statement
The AI learns the humans' concept of legitimacy from them, legitimacy of the process can be lost and sometimes repaired, and the theory characterizes when the AI learns enough about legitimacy to avoid wireheading and manipulation without assuming the humans live forever.
Source
projects/deference/note-dump-2026-08-11/related-chats/2026-07-05__theory-of-legitimacy__c1ab54d9.md, turn 17, the maintainer: "The humans have their legitimacy concept for themselves, and the AI is trying to learn about that. Ideally we'd have some nice theorems characterizing situations where the AI learns enough or doesn't learn enough, telling us about wireheading, human manipulation, etc. This is the version of the theory where we don't just get to assume the humans are immortal and so asymptotic learning can eventually work. The legitimacy of the process can die, and we're trying to address the accumulated odds of that as things evolve."
Notes
A sub-frame grouping the maintainer's questions from the second day of the 2026-07-05 chat with older questions on legitimate deliberation. The grouping is the filing session's. The roots are questions stated as the propositions their positive answers would be, one crux, two literal claims of the maintainer's, one risk story and one plan step he set himself.
Author and human events
Author @smithy-verity; version 1; size L.
No human event has touched this node.
Edges
Above
- part of legitimacy
- root (plan) of legitimacy
Children
- part: legit-learning-theorems leaf/question
- part: legit-accumulated-odds leaf/question
- part: legit-repairable leaf/question
- part: legit-learned-from-others leaf/question
- part: legit-partial-vs-total-corruption leaf/question
- part: self-fulfilling-prophecy-as-corruption cases
- part: legit-corruption-negation leaf/question
- part: counterfactual-oracle-not-special-case leaf/step
- part: legitimate-deliberation-definition leaf/question
- part: legit-endorsement-good-utility leaf/step
- part: deliberation-tracking-assumption leaf/question
- part: basin-of-attraction-legitimacy leaf/question
- part: basin-failure-modes leaf/step
- part: deliberation-fixed-points leaf/question
- part: complete-feedback-formal-model leaf/step
Ranking (the lens, not the structure)
{
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"flags": [],
"V": {
"V_prior": null,
"display": "2",
"headline": 2,
"ref": 2,
"valid": 2,
"weaker": "both"
},
"belief": null,
"draft": "ai",
"root": {
"status": "open",
"Pr": {
"hi": "9/10",
"lo": "0/1",
"mid": "2/5"
},
"value": {
"hi": "1/1",
"lo": "-1/1",
"mid": "2/5"
},
"cost": "240/1",
"margin": "no established leaves"
}
}Say something about it
Each verb opens a prefilled issue; a round files what lands as the ledger event it is.