Right-to-Act: A Pre-Execution Non-Compensatory Decision Protocol for AI Systems
For AI safety and governance, this protocol provides a new abstraction for controlling AI actions by focusing on admissibility rather than optimization, applicable across model architectures.
The paper introduces the Right-to-Act protocol, a deterministic pre-execution decision layer that enforces strict structural constraints to prevent AI-generated decisions from being executed if any required condition is unmet, thereby preserving reversibility and preventing premature actions.
Current AI systems increasingly operate in contexts where their outputs directly trigger real-world actions. Most existing approaches to AI safety, risk management, and governance focus on post-hoc validation, probabilistic risk estimation, or certification of model behavior. However, these approaches implicitly assume that once a decision is produced, it is eligible for execution. In this work, we introduce the Right-to-Act protocol, a deterministic, non-compensatory pre-execution decision layer that evaluates whether an AI-generated decision is permitted to be realized at all. Unlike compensatory systems, where high-confidence signals can override failed conditions, the proposed framework enforces strict structural constraints: if any required condition is unmet, execution is halted or deferred. We formalize the distinction between compensatory and non-compensatory decision regimes and define a pre-execution legitimacy boundary. Through a scenario-based case study, we demonstrate how identical AI outputs can lead to divergent outcomes when evaluated under a Right-to-Act protocol, preserving reversibility and preventing premature or irreversible actions. The proposed approach reframes AI control from optimizing decisions to governing their admissibility, introducing a protocol-level abstraction that operates independently of model architecture or training methodology.