← 研踪 PaperTrailarXiv:2603.05617 · v3
NotAI.AI: An Interactive Explainable System for AI-Generated Text Detection
首次提交 2026-03-06 03:19
版本更新 2026-11-12 06:17
雷达观测 2026-11-13 14:16
摘要
We present NotAI[dot]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[dot]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.
作者备注：11 pages, 5 figures
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v3 · 2026-11-12 06:17 · 最新观测版本
NotAI.AI: An Interactive Explainable System for AI-Generated Text Detection
We present NotAI[dot]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[dot]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.
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