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"Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work

arXiv 2603.07459 ↗First posted 8 Mar 2026cs.HC cs.AIcited by 0

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Abstract (arXiv)

The growing use of AI applications among freelance workers is reshaping trust and relationships with clients. This paper investigates how both workers and clients perceive AI use and disclosure in the freelance economy through a three-stage study: interviews with workers and two survey studies with workers and clients. Findings first reveal a key expectation gap around disclosure: Workers often adopt passive disclosure practices, revealing AI use only when asked, as they assume clients can already detect it. Clients, however, are far less confident in recognizing AI-assisted work and prefer proactive disclosure. A second finding highlights the role of unclear or absent client AI policies, which leave workers consistently misinterpreting clients' expectations for AI use and disclosure. Together, these gaps point to the need for clearer guidelines and practices for AI disclosure. Insights extend beyond freelancing, offering implications for trust, accountability, and policy design in other AI-mediated work domains.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 66 references that are in the map. First posted on arXiv 8 Mar 2026.

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References · 16

  1. 2022Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reductioncited by 137
  2. 2022Human heuristics for AI-generated language are flawedcited by 104
  3. 2024When combinations of humans and AI are useful: A systematic review and meta-analysiscited by 100
  4. 2023How Knowledge Workers Think Generative AI Will (Not) Transform Their Industriescited by 29
  5. 2023Democratic Policy Development using Collective Dialogues and AIcited by 16
  6. 2025Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyondcited by 13
  7. 2025Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creationcited by 13
  8. 2024Generative AI and Perceptual Harms: Who's Suspected of using LLMs?cited by 11
  9. 2024Secret Use of Large Language Model (LLM)cited by 9
  10. 2024Development in times of hype: How freelancers explore Generative AI?cited by 8
  11. 2024PolicyCraft: Supporting Collaborative and Participatory Policy Design through Case-Grounded Deliberationcited by 8
  12. 2024Copying style, Extracting value: Illustrators' Perception of AI Style Transfer and its Impact on Creative Laborcited by 8
4 more
  1. 2024Missing Pieces: How Do Designs that Expose Uncertainty Longitudinally Impact Trust in AI Decision Aids? An In Situ Study of Gig Driverscited by 2
  2. 2024Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Developmentcited by 2
  3. 2022A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI Inequalitycited by 1
  4. 2023Designing Individualized Policy and Technology Interventions to Improve Gig Work Conditionscited by 1

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