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

AI AUTONOMY COEFFICIENT (α): DEFINING BOUNDARIES FOR RESPONSIBLE AI SYSTEMS Nattaya Mairittha∗ nat.mairittha@gmail.com Gabriel Phorncharoenmusikul† gabriel.pcmk@hotmail.com Sorawit Worapradidth‡ rispro.ray@gmail.com 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. Keywords AI autonomy · Human-in-the-Loop (HITL) · Responsible AI · Human-Instead-of-AI (HISOAI) · AI Autonomy Coefficient (α) 1 Introduction The Human-in-the-Loop (HITL) paradigm has been universally adopted as a crucial governance mechanism, founda- tional to embedding Responsible AI (RAI) principles like safety, fairness, and accountability into complex systems. Historically, HITL has served as a strategic intervention for quality control, dataset augmentation, and high-risk decision validation. However, the aggressive commercialization of AI-driven products has exposed a critical systemic vulnerability. We assert that in a growing number of deployed systems, the HITL model has been systematically distorted, transitioning its function from one of strategic oversight to a structural necessity for operational completeness. This paper directly addresses the prevalent practice of deploying human labor not as a refinement loop for the model, but as a mandatory, hidden substitute for non-functional or severely underdeveloped AI components. We formalize this systemic failure as Human-Instead-of-AI (HISOAI). HISOAI is a structural dependency where P(Human →Decision) ≈1 for tasks marketed or promised to be automated, representing a profound failure of AI Deployment Architecture. This practice carries significant ethical and economic costs: it exploits human capital by subjecting workers to precarious, anonymous "ghost work," and it undermines the value proposition of the AI sector by misrepresenting technological capability to consumers and investors. This crisis necessitates a new mandate for AI Transparency and Ethics. ∗Principal Author. The entire manuscript, including the conception of the AFHE framework and the formalization of the α coefficient, was executed and written solely by NM. GP and SW provided essential foundational and domain expertise, were instrumental in validating the research. They are gratefully acknowledged for their invaluable support, as their contribution was critical, though it did not meet the criteria for formal authorship. †Acknowledged for critical operational workflow insights that grounded this research. ‡Acknowledged for essential technical system-failure mapping and architectural validation. arXiv:2512.11295v3 [cs.HC] 18 Dec 2025 We argue that the current HITL interpretation requires a fundamental architectural shift. We propose the AI-First, Human-Empowered (AFHE) design philosophy. AFHE demands that solutions be architected with a verifiable AI-driven core, where the human role is redefined to focus solely on high-value, non-substitutable tasks: model tuning, edge-case validation, ethical oversight, and strategic decision-making. The AFHE framework strictly prohibits human labor from acting as a hidden, costly, and non-scalable substitute for the core AI function. This paper establishes a core requirement: that the integrity of Responsible AI relies on a metric-driven standard for operational autonomy. We formalize this standard through the AI Autonomy Coefficient (α), detailing its mathematical derivation and proposing the AFHE Deployment Algorithm as a required architectural gate. This work contributes the first comprehensive framework to technically differentiate between ethical HITL and deceptive HISOAI, providing the necessary tools to govern the deployment of trustworthy, structurally sound AI-driven products. 2 Related Work This research integrates and critically extends work

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