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

TL;DR

NOTAI.AI is an explainable machine-generated text detector that combines curvature signals, neural, and stylometric features with interpretability tools like SHAP and LLMs for real-time, user-friendly analysis.

Contribution

The paper introduces NOTAI.AI, a novel framework integrating curvature-based signals with neural and stylometric features, enhanced with explainability and real-time interactive capabilities.

Findings

Effective detection of AI-generated text using combined features.

Provides local and global explanations via SHAP and LLMs.

Deployed as an interactive web application for user engagement.

Abstract

We present NOTAI.AI, an explainable framework for machine-generated text detection that extends Fast-DetectGPT by integrating curvature-based signals with neural and stylometric features in a supervised setting. The system combines 17 interpretable features, including Conditional Probability Curvature, ModernBERT detector score, readability metrics, and stylometric cues, within a gradient-boosted tree (XGBoost) meta-classifier to determine whether a text is human- or AI-generated. Furthermore, NOTAI.AI applies Shapley Additive Explanations (SHAP) to provide both local and global feature-level attribution. These attributions are further translated into structured natural-language rationales through an LLM-based explanation layer, which enables user-facing interpretability. The system is deployed as an interactive web application that supports real-time analysis, visual feature inspection, and structured evidence presentation. A web interface allows users to input text and inspect how neural and statistical signals influence the final decision. The source code and demo video are publicly available to support reproducibility.