Data Over Intuition: Measuring the True Drivers of AI Humanization

This deep-dive analysis systematically deconstructs an AI-to-human text transformation pipeline through ablation studies, revealing that filler insertion accounts for 34% of humanization effectiveness while intuitive features like self-correction markers have virtually zero impact. The findings expose a critical gap between human intuition and measurable reality in natural language processing, underscoring why technical decisions must be grounded in quantifiable data rather than assumptions about how people actually speak.