# Generative AI and Perceptual Harms: Who's Suspected of using LLMs?

arXiv: https://arxiv.org/abs/2410.00906 · HTML: https://mlsanity.com/paper/2410.00906/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2024.

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

- [The Widespread Adoption of Large Language Model-Assisted Writing Across Society](https://mlsanity.com/paper/2502.09747/) (2025)
- [Penalizing Transparency? How AI Disclosure and Author Demographics Shape Human and AI Judgments About Writing](https://mlsanity.com/paper/2507.01418/) (2025)
- [Evaluating Style-Personalized Text Generation: Challenges and Directions](https://mlsanity.com/paper/2508.06374/) (2025)
- [On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?](https://mlsanity.com/paper/2510.20810/) (2025)
- [Your voice is your voice: Supporting Self-expression through Speech Generation and LLMs in Augmented and Alternative Communication](https://mlsanity.com/paper/2503.17479/) (2025)
- [Human-Centred LLM Privacy Audits: Findings and Frictions](https://mlsanity.com/paper/2603.12094/) (2026)
- [Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift](https://mlsanity.com/paper/2606.25152/) (2026)
- [LLM or Human? Perceptions of Trust and Information Quality in Research Summaries](https://mlsanity.com/paper/2601.15556/) (2026)
- ["Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work](https://mlsanity.com/paper/2603.07459/) (2026)
- [Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating](https://mlsanity.com/paper/2608.18058/) (2026)
- [Ownership in AI-Assisted Everyday Tasks](https://mlsanity.com/paper/2609.20658/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly integrated into a variety of writing tasks. While these tools can help people by generating ideas or producing higher quality work, like many other AI tools they may risk causing a variety of harms, disproportionately burdening historically marginalized groups. In this work, we introduce and evaluate perceptual harm, a term for the harm caused to users when others perceive or suspect them of using AI. We examined perceptual harms in three online experiments, each of which entailed human participants evaluating the profiles for fictional freelance writers. We asked participants whether they suspected the freelancers of using AI, the quality of their writing, and whether they should be hired. We found some support for perceptual harms against for certain demographic groups, but that perceptions of AI use negatively impacted writing evaluations and hiring outcomes across the board.