The Influence of AI-Like Text on Responses To Disclosure: Evidence From AI Detection Models
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Irons, Charles Austin
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Abstract
The rise of ChatGPT and other generative AI models has revolutionized machine-generated text. One potential application of this technology is helping craft firms’ narrative disclosures. Using two highly rated, commercially available AI detection models, I create novel measures of AI-like text in disclosure based on AI detection models’ classification that the text was generated either wholly or partly by AI. Using these measures, I study changes in disclosure surrounding the release of ChatGPT-4.0 in early 2023 and document a significant increase in the incidence of AI-like text in earnings conference call prepared remarks but not in managers’ responses to questions. Further evidence suggests that AI-like text in disclosure is more common among smaller, younger firms, and, on average, exhibits more positive tone, less uncertainty, and more forward-looking statements than non-AI-like disclosure text. I then compare the market responses to linguistic measures from AI-like disclosure text and non-AI-like disclosure text. Contrary to other studies that find generative AI text to be of higher quality and more persuasive than human text, my evidence suggests that tone in non-AI-like disclosure text is more strongly associated with returns. Overall, my results suggest that AI-like text may mute responses to information in disclosure.
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Date
2025-07-11
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Text
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Dissertation