The paper examines how fine‑tuned Transformer detectors (DistilBERT, RoBERTa) perform on an out‑of‑distribution dataset of hyper‑local news generated by LLMs. Using SHAP‑based explainable AI, the authors uncover three phenomena: a Call‑to‑Action bias, a Specificity Bias where hallucinated named entities evade detection, and a Wikipedia Paradox where formal human prose is misclassified as AI. These results highlight systematic shortcomings of purely statistical detectors in structured human domains and underscore the need for semantically‑aware, interpretable detection methods.
By Nico Morloher, Johannes Reschke, Mohamed Mady
arXiv:2606. 04177v1 Announce Type: cross Abstract: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert users.
By Yassir El Attar, Esra D\"onmez, Maximilian Maurer, Agnieszka Falenska
The paper presents a large‑scale empirical study of 284 interpretable linguistic features to detect AI‑generated English text across 30 language models and 10 text domains. It finds that classifiers using only linguistic features can reliably distinguish machine‑from human‑written text, but most indicators are highly context‑dependent. Only lexical richness measures consistently generalize across model families and domains, offering robust signals for detection.
By Yassir El Attar, Esra D\"onmez, Maximilian Maurer, Agnieszka Falenska
NOTAI.AI is an explainable AI-generated text detection system that goes beyond a simple binary label by showing which signals influenced its prediction. It combines sentence-level conditional probability curvature, a neural detector score, and interpretable stylometric and readability features in an XGBoost meta-classifier, and explains predictions using TreeSHAP feature contributions that can be turned into concise natural-language explanations. Evaluated on a balanced subset of RAID, the full model achieves 0.9685 F1 and receives 94.5–98.6% approval from model judges for the faithfulness of its explanations.
By Oleksandr Marchenko Breneur, Adelaide Danilov, Aria Nourbakhsh, Salima Lamsiyah
Bias in natural language remains a persistent challenge in both human-written and AI-generated content, affecting domains such as journalism, education, and AI research. Most existing detection methods identify only the presence of bias, with limited support for granular detection, interpretable explanations, neutral rewriting, and openly available trained models.
arXiv:2609.15369v1 Announce Type: new
Abstract: Word-level detectors identify unedited AI-generated text almost perfectly, but the literature documents their brittleness under rewording, and a word-l...
By Jochen Madler (Sitefire)
arXiv:2410. 12341v4 Announce Type: replace-cross Abstract: As AI-generated content increasingly populates the web, generative AI models are at growing risk of being trained on their own outputs, a process known as AI autophagy.
By Daniele Gambetta, Gizem Gezici, Fosca Giannotti, Dino Pedreschi, Alistair Knott, Luca Pappalardo
arXiv:2606. 04199v1 Announce Type: cross Abstract: The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strategies.
By Aya Vera-Jimenez, Samuel Jaeger, Calvin Ibenye, Dhrubajyoti Ghosh
arXiv:2606. 00016v1 Announce Type: cross Abstract: Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that rely on surface statistics or likelihood-based signals.
By Aria Nourbakhsh, Adelaide Danilov, Christoph Schommer, Salima Lamsiyah
arXiv:2603. 18482v2 Announce Type: replace-cross Abstract: Standard decoding strategies for text generation, including top-$k$, nucleus sampling, and contrastive search, select tokens based on likelihood, restricting outputs to high-probability regions.
By Esteban Garces Arias, Nurzhan Sapargali, Christian Heumann, Matthias A{\ss}enmacher
The paper introduces SHAKTI, a large-scale Hindi benchmark for detecting AI-generated text, covering multiple genres, LLM families, prompting strategies, and adversarial transformations. It evaluates the benchmark with human annotation and examines linguistic, grammatical, and sentiment differences between human and AI texts. Building on these insights, the authors propose TRIDENT, a round‑trip translation‑based detection framework with three variants that outperforms existing baselines and demonstrates robustness across seven languages and adversarial settings.
By Shifali Agrahari, Sujit Kumar, Sanasam Ranbir Singh
arXiv:2606. 04906v1 Announce Type: cross Abstract: Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detection literature on what constitutes harmful use.
By Nils Dycke, Marina Sakharova, Nico Daheim, Iryna Gurevych