# Penalizing Transparency? How AI Disclosure and Author Demographics Shape Human and AI Judgments About Writing

arXiv: https://arxiv.org/abs/2507.01418 · HTML: https://mlsanity.com/paper/2507.01418/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
- [LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions](https://mlsanity.com/paper/2411.05025/) (2024)
- [Writer-Defined AI Personas for On-Demand Feedback Generation](https://mlsanity.com/paper/2309.10433/) (2023)
- [Collage is the New Writing: Exploring the Fragmentation of Text and User Interfaces in AI Tools](https://mlsanity.com/paper/2405.17217/) (2024)
- [Generative AI and Perceptual Harms: Who's Suspected of using LLMs?](https://mlsanity.com/paper/2410.00906/) (2024)
- [PaperCard for Reporting Machine Assistance in Academic Writing](https://mlsanity.com/paper/2310.04824/) (2023)

## Built on by (most cited first, 5 of 5)

- [More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production](https://mlsanity.com/paper/2601.11072/) (2026)
- [Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs](https://mlsanity.com/paper/2605.29928/) (2026)
- [LLM or Human? Perceptions of Trust and Information Quality in Research Summaries](https://mlsanity.com/paper/2601.15556/) (2026)
- [Struggle Premium : How Human Effort and Imperfection Drive Perceived Value in the Age of AI](https://mlsanity.com/paper/2604.15324/) (2026)
- [Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty](https://mlsanity.com/paper/2609.03460/) (2026)

## Abstract (arXiv)

As AI integrates in various types of human writing, calls for transparency around AI assistance are growing. However, if transparency operates on uneven ground and certain identity groups bear a heavier cost for being honest, then the burden of openness becomes asymmetrical. This study investigates how AI disclosure statement affects perceptions of writing quality, and whether these effects vary by the author's race and gender. Through a large-scale controlled experiment, both human raters (n = 1,970) and LLM raters (n = 2,520) evaluated a single human-written news article while disclosure statements and author demographics were systematically varied. This approach reflects how both human and algorithmic decisions now influence access to opportunities (e.g., hiring, promotion) and social recognition (e.g., content recommendation algorithms). We find that both human and LLM raters consistently penalize disclosed AI use. However, only LLM raters exhibit demographic interaction effects: they favor articles attributed to women or Black authors when no disclosure is present. But these advantages disappear when AI assistance is revealed. These findings illuminate the complex relationships between AI disclosure and author identity, highlighting disparities between machine and human evaluation patterns.