arXiv:2601. 11072v1 Announce Type: cross Abstract: Within journalistic editorial processes, disclosing AI usage is currently limited to simplistic labels, which misses the nuance of how humans and AI collaborated on a news article.
arXiv:2606. 11116v1 Announce Type: cross Abstract: As newsrooms integrate generative AI, journalists face a disclosure challenge: how to communicate AI involvement in ways that maintain reader trust.
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. 29437v1 Announce Type: cross Abstract: The growing use of Large Language Models (LLMs) in education, software engineering, academic writing, and technical documentation raises a key question: how can we evaluate not only AI-assisted outputs, but also the interaction process that produced them?
arXiv:2608. 11794v1 Announce Type: cross Abstract: The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content provenance and AI involvement.
The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content provenance and AI involvement. But the effects of such disclosures remain uncertain.
The article reviews two decades of empirical research on artificial intelligence in media, highlighting how AI will continuously reshape journalistic work. It identifies key social and epistemological challenges, such as increased reliance on tech platforms, threats to editorial independence, and journalists’ ambivalence between job security and creative liberation. The study argues that understanding AI’s impact on audiences and journalists is essential for guiding its responsible use in journalism.
arXiv:2606. 11835v1 Announce Type: cross Abstract: Collecting participants' lived experiences is central to design research.
The paper introduces Provenance Density, an interface that visualizes the density of verified claims within a text to counter the Fluency Trap—where users mistake fluent AI-generated hallucinations for truth. In a study with 81 participants, the interface significantly improved users’ ability to distinguish true from fabricated content, while no signal led to no discernment. A technical audit of 200 samples revealed that retrieval density alone is insufficient, and that the Consistency Veto provides most of the discriminative power for dynamic queries.
arXiv:2606. 24635v1 Announce Type: cross Abstract: Traditional visual data storytelling relies on binary graphics that depict two simplified groups in conflict.
arXiv:2608.28637v1 Announce Type: new Abstract: Autonomous scientific discovery systems can generate large numbers of research ideas, experiments, and manuscripts with minimal human intervention. As...
Conversational DNA is a visual language and interactive atlas designed to explore human and AI dialogue by mapping speaker strands, communicative bases, and directed pairings. It visualizes speaker switching, response distance, and contribution length through adjustable helix geometry, and covers 151,489 episodes across eight corpora totaling 1.57 million source records. The system improves precision@5 on Molweni motif queries from 58.8% to 77.2% and demonstrates how annotation coverage affects perceived collection differences.
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...