Loper Bright Enterprises v. Raimondo, 603 U.S. 369 (28 June 2024), is the documented legal-error path the essay does not use.
New England herring boats challenged a National Marine Fisheries Service rule that made them pay for at-sea observers. The Supreme Court, 6–3, Chief Justice Roberts writing, overruled Chevron U.S.A. Inc. v. NRDC, 467 U.S. 837 (1984). Under the Administrative Procedure Act, courts "must exercise independent judgment in deciding whether an agency has acted within its statutory authority" and "may not defer to an agency interpretation of the law simply because a statute is ambiguous." Prior cases decided under Chevron remain, as statutory stare decisis; Skidmore respect for an agency's view is still available.
Fishermen got a case, a court, and a changed legal rule about who may say what a statute means. That is grounded contradiction traversing into lawful correction. It is not the essay's full chain. The observer-pay programme still goes back to the lower courts as a question of the Magnuson-Stevens Act's best reading. Loper Bright does not name an implementation owner for fisheries policy, a deadline to change observer funding, or a later test of whether the fleet is safer. Score it as a win on legal meaning and a blank on whether the operational rule moved.
The AI literature already contains two models, and they disagree with this case about what "corrigible" is.
Soares, Fallenstein, Yudkowsky, and Armstrong, "Corrigibility" (AAAI 2015 workshop): an agent is corrigible if it cooperates with what its creators regard as a corrective intervention — shutdown or modification — despite the usual reasons a capable optimizer has to resist. Their shutdown-button construction, built on Armstrong's utility indifference, failed some of their own list: it would not keep shutdown behaviour in sub-agents, and it did not care about keeping the button in working order.
Hadfield-Menell, Dragan, Abbeel, and Russell, "The Off-Switch Game" (2016), is a different bet. If the robot is uncertain about the human's utility, the human reaching for the off-switch is evidence, so the robot has a reason to allow it. Ryan Carey, "Incorrigibility in the CIRL Framework" (AIES 2018; arXiv:1709.06275), then showed that this incentive is not robust to a misspecified reward model. A programmer error in the parameterized reward can remove the incentive to follow shutdown.
Chevron let the operator — the agency — update the meaning of an ambiguous statute. Loper Bright takes that update away and gives it to courts. That is closer to Soares (an external corrector may intervene) than to the Off-Switch Game (the agent stays corrigible because it is uncertain and treats the human as informative). A ministry that is sure of its reading is, on Russell's model, exactly the agent that will resist. Loper Bright does not make agencies uncertain. It makes their certainty less legally conclusive.
Discriminator: Off-Switch predicts correction when the incumbent treats the challenger as information about the objective. Loper Bright predicts correction when a different institution may read the statute even if the incumbent is certain. Those are different machines. The essay's five-role split is the second. It is not a translation of CIRL.
He already splits three error classes: a mechanism contradiction against the policy's own claim, a legal or constitutional contradiction against a higher rule, and a political fight over values. Loper Bright is the second. The Off-Switch Game is a story about the first, and only if there is one human whose action carries information about one objective.
That split is not what is still open. What is still open is whether a class-two win counts as the state being corrigible. If it does, Loper Bright is a completed legal trace and the live object is class one: a stated causal claim that fails, with no rights violation and no election, still able to change the programme. If it does not, the essay is asking for something constitutions have mostly not built.