AI Autonomy Coefficient ($\alpha$): Defining Boundaries for Responsible AI Systems

TL;DR

This paper introduces the AI Autonomy Coefficient to quantify AI system independence, promoting responsible AI deployment by ensuring systems meet a minimum autonomy threshold to prevent unethical human dependency.

Contribution

The paper proposes the AFHE paradigm and the AI Autonomy Coefficient as novel tools for measuring and enforcing AI system independence before deployment.

Findings

HISOAI systems have an autonomy level of 0.38

AFHE systems achieve an autonomy level of 0.85

The AI Autonomy Coefficient effectively identifies dependent AI systems

Abstract

The integrity of many contemporary AI systems is compromised by the misuse of Human-in-the-Loop (HITL) models to obscure systems that remain heavily dependent on human labor. We define this structural dependency as Human-Instead-of-AI (HISOAI), an ethically problematic and economically unsustainable design in which human workers function as concealed operational substitutes rather than intentional, high-value collaborators. To address this issue, we introduce the AI-First, Human-Empowered (AFHE) paradigm, which requires AI systems to demonstrate a quantifiable level of functional independence prior to deployment. This requirement is formalized through the AI Autonomy Coefficient, measuring the proportion of tasks completed without mandatory human intervention. We further propose the AFHE Deployment Algorithm, an algorithmic gate that enforces a minimum autonomy threshold during offline evaluation and shadow deployment. Our results show that the AI Autonomy Coefficient effectively identifies HISOAI systems with an autonomy level of 0.38, while systems governed by the AFHE framework achieve an autonomy level of 0.85. We conclude that AFHE provides a metric-driven approach for ensuring verifiable autonomy, transparency, and sustainable operational integrity in modern AI systems.