Research Article (preprint): “Readers Prefer Outputs of AI Trained on Copyrighted Books Over Expert Human Writers”

The article (preprint) shared below was recently posted on arXiv.

Title

Readers Prefer Outputs of AI Trained on Copyrighted Books Over Expert Human Writers

Authors

Tuhin Chakrabarty
Department of Computer Science and AI Innovation Institute, Stony Brook University

Jane C. Ginsburg
Columbia Law School

Paramveer Dhillon
School of Information Science, University of Michigan
MIT Initiative on the Digital Economy

Source

via arXiv
DOI: 10.48550/arXiv.2510.13939

Abstract

The use of copyrighted books for training AI models has led to numerous lawsuits from authors concerned about AI’s ability to generate derivative this http URL it’s unclear whether these models can generate high quality literary text while emulating authors’ styles. To answer this we conducted a preregistered study comparing MFA-trained expert writers with three frontier AI models: ChatGPT, Claude & Gemini in writing up to 450 word excerpts emulating 50 award-winning authors’ diverse styles. In blind pairwise evaluations by 159 representative expert & lay readers, AI-generated text from in-context prompting was strongly disfavored by experts for both stylistic fidelity (OR=0.16, p<10^8) & writing quality (OR=0.13, p<10^7) but showed mixed results with lay readers. However, fine-tuning ChatGPT on individual authors’ complete works completely reversed these findings: experts now favored AI-generated text for stylistic fidelity (OR=8.16, p<10^13) & writing quality (OR=1.87, p=0.010), with lay readers showing similar shifts. These effects generalize across authors & styles. The fine-tuned outputs were rarely flagged as AI-generated (3% rate v. 97% for in-context prompting) by best AI detectors. Mediation analysis shows this reversal occurs because fine-tuning eliminates detectable AI stylistic quirks (e.g., cliche density) that penalize in-context outputs. While we do not account for additional costs of human effort required to transform raw AI output into cohesive, publishable prose, the median fine-tuning & inference cost of $81 per author represents a dramatic 99.7% reduction compared to typical professional writer compensation. Author-specific fine-tuning thus enables non-verbatim AI writing that readers prefer to expert human writing, providing empirical evidence directly relevant to copyright’s fourth fair-use factor, the “effect upon the potential market or value” of the source works.

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