Generative AI and Perceptual Harms: Who's Suspected of using LLMs?
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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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2024.
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Citing papers per year: 2025: 5; 2026: 6.
11 papers.
- 2025The Widespread Adoption of Large Language Model-Assisted Writing Across Societycited by 50
- 2025Penalizing Transparency? How AI Disclosure and Author Demographics Shape Human and AI Judgments About Writingcited by 5
- 2025On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?cited by 3
- 2025Evaluating Style-Personalized Text Generation: Challenges and Directionscited by 2
- 2025Your voice is your voice: Supporting Self-expression through Speech Generation and LLMs in Augmented and Alternative Communicationcited by 1
- 2026Human-Centred LLM Privacy Audits: Findings and Frictionscited by 1
- 2026Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shiftcited by 1
- 2026LLM or Human? Perceptions of Trust and Information Quality in Research Summariescited by 0
- 2026"Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Workcited by 0
- 2026Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Datingcited by 0
- 2026Ownership in AI-Assisted Everyday Taskscited by 0
- 2026Ownership in AI-Assisted Everyday Tasks17 Sep 2026
- 2026Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating18 Aug 2026
- 2026Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift23 Jun 2026
- 2026Human-Centred LLM Privacy Audits: Findings and Frictions12 Mar 2026
- 2026"Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work8 Mar 2026
- 2026LLM or Human? Perceptions of Trust and Information Quality in Research Summaries22 Jan 2026
- 2025On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?23 Oct 2025
- 2025Evaluating Style-Personalized Text Generation: Challenges and Directions8 Aug 2025
- 2025Penalizing Transparency? How AI Disclosure and Author Demographics Shape Human and AI Judgments About Writing2 Jul 2025
- 2025Your voice is your voice: Supporting Self-expression through Speech Generation and LLMs in Augmented and Alternative Communication21 Mar 2025
- 2025The Widespread Adoption of Large Language Model-Assisted Writing Across Society13 Feb 2025