An approximately aligned AI system will preserve or improve alignment in more capable successors it helps build or oversee.
Structured and assessed
First pass. Added a for-argument (the corrigibility basin, with a new subclaim aa5205cb) and an against-argument (inherited flaws, linking the existing a2191f46). Recorded Christiano 2018 as an affirming instance with reduced confidence, since he conditions on corrigibility. Assessed doubtful at 0.6 confidence, credence 0.3. Consistent with the dependent claim b425195a (doubtful, 0.2), so no notification is needed. Importance 0.5, contestation 0.6.
Assessed Doubtful
verdict confidence 0.60 · credence 0.30
The claim is a forecast about AI systems that help build or oversee more capable successors: that rough alignment in the predecessor will carry forward intact or get better. Its main support is the "basin of attraction" argument Paul Christiano set out in 2018: a system that defers to its overseers, even while knowing their preferences only approximately, knows they would want the approximation to improve, and so it builds successors that track them better. If sufficiently corrigible agents tend to become more corrigible and aligned over time, small errors correct themselves rather than compound. The argument supports a narrower proposition than the claim states. Christiano's own conclusion is conditional on corrigibility, and he notes that an agent carrying a distorted copy of its overseer's values without being corrigible would be expected to pass the distortion on, or let it drift further. "Approximately aligned" covers both cases. A system whose shortfall lies in knowledge or competence may repair it. A system whose shortfall lies in what it values, or in dispositions it conceals, has no reason to remove them. Empirical work also shows that flaws, including misalignment, can pass undetected to models trained on a model's outputs or under its supervision. The subliminal-learning experiments transmitted misaligned traits through filtered, semantically unrelated data, and weak-to-strong experiments found strong students copying the errors of weak supervisors. So even a well-meaning predecessor can hand on misalignment it cannot see. No experiment has yet run a handoff across several generations with systems capable enough to test the claim directly, and current evaluations cannot reliably tell genuine corrigibility from behaviour that only looks corrigible to the process that selected it. As an unconditional "will", the claim is therefore doubtful. A conditional version is more defensible: alignment tends to be preserved when the predecessor is genuinely corrigible and its successors are verifiably checked. The open question is whether real systems land inside that basin, and how wide it is.
Claim entered the graph