# Envisioning the Applications and Implications of Generative AI for News Media

arXiv: https://arxiv.org/abs/2402.18835 · HTML: https://mlsanity.com/paper/2402.18835/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2024.

## Built on by (most cited first, 11 of 11)

- [Bias Amplification: Large Language Models as Increasingly Biased Media](https://mlsanity.com/paper/2410.15234/) (2024)
- ["Ownership, Not Just Happy Talk": Co-Designing a Participatory Large Language Model for Journalism](https://mlsanity.com/paper/2501.17299/) (2025)
- [Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles](https://mlsanity.com/paper/2406.10773/) (2024)
- [LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content](https://mlsanity.com/paper/2410.15308/) (2024)
- [Survey of User Interface Design and Interaction Techniques in Generative AI Applications](https://mlsanity.com/paper/2410.22370/) (2024)
- [AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models](https://mlsanity.com/paper/2410.07561/) (2024)
- [More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production](https://mlsanity.com/paper/2601.11072/) (2026)
- [Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making](https://mlsanity.com/paper/2502.14869/) (2025)
- [Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems](https://mlsanity.com/paper/2507.21157/) (2025)
- [Towards Real-World Validity in Generative AI Benchmarks: Understanding and Designing Domain-Centered Evaluations for Journalism Practitioners](https://mlsanity.com/paper/2511.05501/) (2025)
- [LLMs Should Incorporate Explicit Mechanisms for Human Empathy](https://mlsanity.com/paper/2604.10557/) (2026)

## Abstract (arXiv)

This article considers the increasing use of algorithmic decision-support systems and synthetic media in the newsroom, and explores how generative models can help reporters and editors across a range of tasks from the conception of a news story to its distribution. Specifically, we draw from a taxonomy of tasks associated with news production, and discuss where generative models could appropriately support reporters, the journalistic and ethical values that must be preserved within these interactions, and the resulting implications for design contributions in this area in the future. Our essay is relevant to practitioners and researchers as they consider using generative AI systems to support different tasks and workflows.