# Right-to-Act: A Pre-Execution Non-Compensatory Decision Protocol for AI Systems

arXiv: https://arxiv.org/abs/2604.24153 · HTML: https://mlsanity.com/paper/2604.24153/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 27 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [Runtime Governance for AI Agents: Policies on Paths](https://mlsanity.com/paper/2603.16586/) (2026)
- [Before the Tool Call: Deterministic Pre-Action Authorization for Autonomous AI Agents](https://mlsanity.com/paper/2603.20953/) (2026)
- [Bounding the Black Box: A Statistical Certification Framework for AI Risk Regulation](https://mlsanity.com/paper/2604.21854/) (2026)

## Abstract (arXiv)

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.