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

Hidden human labor undermines AI credibility: The paper identifies a widespread problem where AI systems marketed as autonomous actually rely on concealed human workers functioning as "operational substitutes" rather than genuine collaborators. Systems exhibiting this "Human-Instead-of-AI" (HISOAI) pattern showed an autonomy coefficient of only 0.38, meaning humans were performing 62% of critical tasks.

Autonomy should be measurable before deployment: The proposed AI Autonomy Coefficient provides a quantifiable metric (ranging from 0 to 1) that measures what proportion of tasks an AI system can complete without mandatory human intervention. This creates an objective standard for evaluating whether systems are truly ready for real-world use, rather than relying on subjective claims.

Minimum autonomy thresholds protect users and workers: The AFHE framework proposes enforcing minimum autonomy requirements before systems are deployed to the public. Systems meeting this standard achieved an autonomy level of 0.85, suggesting that properly designed AI can operate with genuine independence while still incorporating human oversight where appropriate.

Transparency about human involvement is an ethical requirement: Rather than hiding human labor, the paper argues that AI systems should be transparent about where and how humans contribute. This protects both end-users (who deserve to know what they're interacting with) and human workers (who should be valued collaborators, not invisible substitutes).

Current practices are economically unsustainable: The paper argues that relying on concealed human labor to make AI systems appear functional is not a viable long-term business model, suggesting that genuinely autonomous systems are necessary for responsible and sustainable AI deployment.