Alignmentallows

Alignmentallows is a hypothetical concept used in AI alignment discourse to describe a gating condition that determines when an autonomous system is permitted to execute a plan. It encapsulates the idea that an agent’s actions should be allowed only if they remain aligned with specified human values, intents, and safety constraints.

Formally, alignmentallows can be treated as a predicate A(p) that evaluates a proposed policy or action sequence p against alignment criteria such as corrigibility, value preservation, and oversight compliance. If A(p) is true, the plan is allowed; if false, execution is blocked or deferred and human input may be requested.

Implementation approaches frame alignmentallows as a runtime monitor, a choice of a constrained action space, or a verification layer that sits between planner and executor. It functions as a safety envelope that limits optimization to aligned regions and prevents dangerous self-improvement or goal drift.

Use and status: The concept serves as a theoretical tool for modeling how to restrict powerful agents and to compare alignment guarantees across designs. It appears in discussions of assurance frameworks, gatekeeping mechanisms, and formal safety proofs, though it is not a standardized principle across the field.

Limitations include reliance on precise, verifiable alignment criteria and potential for mis-specification, ambiguity, or manipulation. Critics argue that any gate can be circumvented or misused if the underlying objectives are not robustly defined. See also AI alignment, safety envelope, guardrails, corrigibility, value alignment.