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:2409. 03500v4 Announce Type: replace-cross Abstract: The increasing use of artificial intelligence (AI) in news production raises important questions about how audiences perceive and respond to AI-generated journalism.
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.
arXiv:2606.29121v2 Announce Type: replace-cross Abstract: Public discourse about artificial intelligence (AI) often uses anthropomorphic language: language that attributes human capabilities and char...
arXiv:2504.05008v3 Announce Type: replace Abstract: The rapid development of AI-driven tools, particularly large language models (LLMs), is reshaping professional writing. Still, key aspects of their...
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 study examines how users’ stated preferences for AI assistant responses align with the actual rewards they give to those responses. Using the PRISM dataset, researchers compared 1,396 participants’ reported priorities on seven response attributes with their ratings of 29,347 responses from 21 large language models. They found that features not explicitly surveyed—such as length, formatting, and refusal language—were as strongly linked to ratings as the surveyed attributes, and that written feedback and prior conversation context influenced how much weight participants gave to length, while overall stated priorities poorly predicted the concrete features that drive ratings.
Knowing when to say "I don't know" is fundamental to human judgment, yet AI assistants offer a fluent answer to almost any question. In five experiments (N = 3,132; four preregistered, one direct replication), participants answered difficult questions and could always decline to respond.
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:2607. 14729v1 Announce Type: cross Abstract: Recent findings suggest that detection models for artificial intelligence (AI) cannot accurately identify AI-generated text and may exhibit bias against certain minority groups.
The study investigates how users’ stated preferences for AI-generated responses align with the actual features they reward. Using the PRISM dataset, researchers compared participants’ reported priorities for seven response attributes with their ratings of over 29,000 responses from 21 large language models. They found that attributes not explicitly surveyed—such as length, formatting, and refusal language—were as strongly linked to ratings as the surveyed ones, and that written feedback was a better predictor of how users weighted these concrete features than their stated priorities.
arXiv:2606. 01929v1 Announce Type: new Abstract: Public discourse on AI has become polarized; exaggerated positions on AI in traditional and social media threaten the development of AI Literacy among the general public.