# Voices of Freelance Professional Writers on AI: Limitations, Expectations, and Fears

arXiv: https://arxiv.org/abs/2504.05008 · HTML: https://mlsanity.com/paper/2504.05008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Survey on Causal-based Machine Learning Fairness Notions](https://mlsanity.com/paper/2010.09553/) (2020)
- [POTs: Protective Optimization Technologies](https://mlsanity.com/paper/1806.02711/) (2018)
- [Ethics-Based Auditing to Develop Trustworthy AI](https://mlsanity.com/paper/2105.00002/) (2021)
- [Metaethical Perspectives on 'Benchmarking' AI Ethics](https://mlsanity.com/paper/2204.05151/) (2022)
- [Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?](https://mlsanity.com/paper/2105.01441/) (2021)
- [Operationalising AI governance through ethics-based auditing: An industry case study](https://mlsanity.com/paper/2407.06232/) (2024)
- [Navigating Fairness Measures and Trade-Offs](https://mlsanity.com/paper/2307.08484/) (2023)

## Abstract (arXiv)

The rapid development of AI-driven tools, particularly large language models (LLMs), is reshaping professional writing. Still, key aspects of their adoption such as language support, ethics, and long-term impact on writers' voice and creativity remain underexplored. In this work, we carried out a questionnaire (N = 301) and an interactive task (N = 36) targeting freelance professional writers regularly using AI. We examined AI-assisted writing practices across 25+ languages, ethical concerns, and user expectations. Our findings reveal that adoption is shaped more by peer influence and professional outlook than demographics, that multilingual writers face both performance and perception barriers to equitable AI use, and that job security concerns are widespread and adoption-independent.