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arXiv · 2603.05617

NOTAI.AI: Explainable Detection of Machine-Generated Text via Curvature and Feature Attribution

Oleksandr Marchenko Breneur·Adelaide Danilov·Aria Nourbakhsh·Salima Lamsiyah
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Abstract

We present NotAI.AI, an explainable AI-generated text detection system. Instead of returning only a binary label or confidence score, the system shows which signals influenced the prediction and lets users inspect an attribution-based sensitivity estimate obtained by subtracting selected local contributions. NotAI.AI combines sentence-level conditional probability curvature, a neural detector score, and interpretable stylometric and readability features in an XGBoost meta-classifier. It explains predictions with TreeSHAP feature contributions and can turn the resulting evidence into a concise natural-language explanation. We evaluate the system on a category-balanced subset of RAID containing human-written, clean AI-generated, and attacked AI-generated texts. The full model outperforms variants based on individual feature families, reaching 0.9685 F1 on the held-out within-subset test split. In an automatic evaluation, two model judges rate 94.5-98.6\% of generated explanations as faithful to the supplied detector evidence. The web interface (https://notai-ai.vercel.app), source code (https://github.com/Oleksandr-MB/EMNLP2026DEMO_NotAI.AI), and demonstration video(https://youtu.be/l8Nk8kdBTHQ) are publicly available.

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Oleksandr Marchenko Breneur, Adelaide Danilov, Aria Nourbakhsh, Salima Lamsiyah. 2026-09-03. NOTAI.AI: Explainable Detection of Machine-Generated Text via Curvature and Feature Attribution. https://arxiv.org/abs/2603.05617

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