Detailed answer
Key answer: AI alignment and LERA address related but different problems.
Core explanation
AI alignment and LERA address related but different problems.
Alignment asks whether an AI system behaves in ways consistent with human intentions, values, objectives, or instructions.
LERA asks whether a proposed action should be allowed to become execution.
Alignment is important because a system that pursues the wrong objective may generate harmful outputs. But even an aligned system can propose an action that should not be executed.
This can happen because:
- the information is incomplete;
- the situation has changed;
- the action is irreversible;
- the user lacks proper authority;
- the action violates a domain rule;
- responsibility is unclear;
- the consequence exceeds the permitted scope;
- the system’s recommendation is valid in general but inappropriate in this specific context.
For example, an aligned AI may be instructed to maximize the operational continuity of a factory. During an emergency, it may recommend keeping production online.
That recommendation may be consistent with the objective it was given. But if continuing production increases fire risk, endangers workers, violates emergency rules, or damages critical equipment, the action should not execute.
Alignment helps the system pursue intended objectives.
LERA governs whether pursuing that objective may become action under the present conditions.
The distinction can be expressed simply:
Alignment asks whether the AI is pursuing the intended direction.
LERA asks whether the proposed action may cross the execution boundary.
LERA therefore complements alignment rather than competing with it.
A well-aligned system is stronger when its outputs still pass through judgment, authority, responsibility, and reliability-rule review before execution.
Without LERA, alignment may remain dependent on model behavior.
With LERA, even aligned output remains subject to execution governance.