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AI-generated lemons: a sour outlook for content producers?

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  • Howell, Bronwyn E.
  • Potgieter, Petrus H.

Abstract

Artificial intelligence (AI) techniques for natural language processing have made dramatic advances in the past few years (Lin 2023). Thunström & Steingrimsson (2022) demonstrated that the present generation of AI text engines are even able to write low-level scientific pieces about themselves, with relatively minimal prompting, whereas Goyal et al. (2022) show how good general-purpose AI language engines are at summarizing news articles. There is however a downside to all of this progress. Bontridder & Poullet (2021) point out how inexpensive it has become to generate deepfake videos and synthetic voice recordings. Kreps et al. (2022) look at AI generated text and find that "individuals are largely incapable of distinguishing between AI- and human-generated text". Illia et al. (2023) point to three ethical challenges raised by automated text generation that is difficult to distinguish from human writing: 1. facilitation of mass manipulation and disinformation; 2. a lowest denominator problem where a mass of low-quality but incredibly cheap text, crowds out higher-quality discourse; and 3. the suppression of direct communication between stakeholders and an attendant drop in the levels of trust. Our focus is mainly on (2) and we examine the institutional consequences that may arise in two specific sectors currently already facing challenges from AI-generated text: scientific journals and social media platforms. Drawing on the body of learning from institutional economics regarding responses to uncertainties in the veracity of information, it also proposes some elementary remedies that may prove helpful in navigating through the anticipated challenges. Distinguishing genuinely human-authored content from machine-generated text will likely be more easily done using a credible signal of the authenticity of the content creator. This is a variation of Akerlof's (1970) famous "market for lemons" problem. This paper uses an inductive approach to examine sections of the content industry that are likely to be particularly relevant to "market for lemons" substitution, referring to the framework of Giannakas & Fulton (2020).

Suggested Citation

  • Howell, Bronwyn E. & Potgieter, Petrus H., 2023. "AI-generated lemons: a sour outlook for content producers?," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277971, International Telecommunications Society (ITS).
    • Handle: RePEc:zbw:itse23:277971
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    References listed on IDEAS

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    1. Michael Park & Erin Leahey & Russell J. Funk, 2023. "Papers and patents are becoming less disruptive over time," Nature, Nature, vol. 613(7942), pages 138-144, January.
    2. Jennifer Byrne, 2019. "We need to talk about systematic fraud," Nature, Nature, vol. 566(7742), pages 9-9, February.
    3. Holly Else, 2023. "Abstracts written by ChatGPT fool scientists," Nature, Nature, vol. 613(7944), pages 423-423, January.
    4. Benoît Dupont & Anne-Marie Côté & Claire Savine & David Décary-Hétu, 2016. "The ecology of trust among hackers," Global Crime, Taylor & Francis Journals, vol. 17(2), pages 129-151, April.
    5. Guillaume Cabanac & Cyril Labbé, 2021. "Prevalence of nonsensical algorithmically generated papers in the scientific literature," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(12), pages 1461-1476, December.
    6. Adam Day, 2022. "Exploratory analysis of text duplication in peer-review reveals peer-review fraud and paper mills," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5965-5987, October.
    7. Alessandro Checco & Lorenzo Bracciale & Pierpaolo Loreti & Stephen Pinfield & Giuseppe Bianchi, 2021. "AI-assisted peer review," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-11, December.
    8. Chengcheng Shao & Giovanni Luca Ciampaglia & Onur Varol & Kai-Cheng Yang & Alessandro Flammini & Filippo Menczer, 2018. "The spread of low-credibility content by social bots," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    9. Engers, Maxim & Gans, Joshua S, 1998. "Why Referees Are Not Paid (Enough)," American Economic Review, American Economic Association, vol. 88(5), pages 1341-1349, December.
    10. Vanja Pupovac, 2021. "The frequency of plagiarism identified by text-matching software in scientific articles: a systematic review and meta-analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8981-9003, November.
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