The study shows that when people enjoy an artwork, they are more likely to believe it was created by a human, regardless of whether it was actually AI-generated or not. In a large online Turing Test, 22.1 percentage points more of respondents who favored a picture thought it was human-made, and this pattern held across all images and detection methods. Even those who were skeptical of AI art often preferred AI-generated pieces but still attributed them to humans, indicating a strong link between aesthetic preference and perceived authorship.
arXiv:2510.08831v2 Announce Type: replace Abstract: As AI writing tools become widespread, we need to understand how both humans and machines evaluate literary style, a domain where objective standar...
arXiv:2609.15511v1 Announce Type: cross Abstract: This paper investigates the generation and human evaluation of Japanese haiku by contemporary Large Language Models (LLMs), focusing on authorship pe...
The paper audits 27 text‑to‑image generators released between 2022 and 2026, examining 51,300 AI‑generated faces and 160,196 real photographs. Using automated metrics and 550 human raters, it finds that AI‑generated faces consistently score higher on attractiveness than real faces, with female‑prompted images often surpassing professional models and darker‑skinned groups receiving lower scores. A preregistered hiring simulation shows that exposure to AI‑generated attractive faces reduces hireability and trustworthiness ratings for real candidates, whereas retouched photographs have no such effect.
Hundreds of millions use AI for writing assistance, and a study with 2,939 writers and 11,091 readers examined how AI affects perceptions of writers. Writers who used AI were seen as more opinionated, competent, positive, and demographically privileged, though many writers objected to these distortions yet still preferred AI‑assisted text. Reward models reduced objectionable distortions but lowered user acceptance, and readers trusted and were more persuaded by AI‑assisted writers when distortions were higher.
arXiv:2509. 02910v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly act on people's behalf: they write emails, buy groceries, and book restaurants.
arXiv:2607. 17826v1 Announce Type: cross Abstract: Despite the growing availability of customizable social artificial intelligence (AI), such as ChatGPT, Grok, and Character.
arXiv:2606. 12350v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) raises critical questions about human creativity and individual expression in an era of AI-assisted creation.
arXiv:2610.34770v1 Announce Type: cross Abstract: Direct messaging is often employed by social media content creators to directly interact with followers. Those creators attempt to engage with follow...
The paper shows that in text‑to‑image evaluation, human preference for attractive faces can outweigh objective image quality, leading to biased rankings and model outputs. A user study revealed that annotators consistently rate more attractive faces as higher quality, even when comparing real photographs of equal fidelity. This bias can invert leaderboard rankings, dominate preference model scores, and cause generated faces to shift toward younger, more feminine features, undermining user control.
arXiv:2601. 15797v2 Announce Type: replace Abstract: Many theorists maintain that conscious intentional agency is a necessary condition of creativity.
Despite the growing availability of customizable social artificial intelligence (AI), such as ChatGPT, Grok, and Character. ai, we know little about how users actively shape social AI to reflect their personal preferences.
arXiv:2606. 29121v1 Announce Type: cross Abstract: Public discourse about artificial intelligence (AI) often uses anthropomorphic language: language that attributes human capabilities and characteristics to the system.