Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Correspondence
  • Published:

Addressing the harms of AI-generated inauthentic content

Nature Machine Intelligence volume 5, pages 679–680 (2023)Cite this article

Subjects

Generative AI tools lower the cost of generating false but credible content at scale1, defeating the already weak moderation defenses of social media platforms. Using inauthentic accounts and other tricks to exploit algorithmic and socio-cognitive vulnerabilities, bad actors can monetize false and harmful content, commit fraud and manipulate opinions for political gains2,3. Generative AI tools such as ChatGPT make it easier to create large volumes of false (but convincing) social media profiles and content. Narratives can even be tailored to a particular community by an inauthentic influence campaign. For example, through health disinformation, a foreign adversary can make an entire population more vulnerable to a future pandemic4.

Image-generation models can create fake profile pictures that are indistinguishable from real people. It is occasionally possible to spot certain AI-generated images and text through glitches. For example, some generative adversarial network (GAN)-generated profiles can be detected from the positions of the eyes. Similarly, ChatGPT-generated content has been flagged from text patterns such as “as an AI model.…” These are obvious signatures that the ChatGPT API has been used to automatically create and post content. But even as these glitches can now be found everywhere, they reveal what is likely to be only the tip of an iceberg. Before our lab developed tools to detect social bots 10 years ago5, there was little awareness of bot manipulation. Similarly, we currently have little awareness of the volume of inauthentic behaviour supported by AI.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

References

  1. Marcus, G. Comm. ACM https://cacm.acm.org/blogs/blog-cacm/267674-ais-jurassic-park-moment/fulltext (2022).

  2. Menczer, F. & Hills, T. Sci. Am. 323(6), 54–61 (2020).

    Google Scholar 

  3. David, M. J. et al. Science 359, 1094–1096 (2018).

    Article  Google Scholar 

  4. Pierri, F. et al. Sci. Rep. 12, 5966 (2022).

    Article  Google Scholar 

  5. Ferrara, E., Varol, O., Davis, C., Menczer, F. & Flammini, A. Comm. ACM 59(7), 96–104 (2016).

    Article  Google Scholar 

  6. Else, H. Nature 613, 423 (2023).

    Article  Google Scholar 

  7. Pacheco, D. et al. in Proc. International AAAI Conference on Web and Social Media vol. 15 (eds. Pacheco, D. et al.) 455–466 (2012).

  8. Yasseri, T. & Menczer, F. Preprint at https://doi.org/10.48550/arXiv.2104.13754 (2023).

  9. van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R. & Bockting, C. L. Nature 614, 224–226 (2023).

    Article  Google Scholar 

  10. Goldstein, J. A. et al. Preprint at https://doi.org/10.48550/arXiv.2301.04246 (2023).

Download references

Author information

Authors and Affiliations

  1. Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Filippo Menczer, David Crandall, Yong-Yeol Ahn & Apu Kapadia

Authors
  1. Filippo Menczer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Crandall
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong-Yeol Ahn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Apu Kapadia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Filippo Menczer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Menczer, F., Crandall, D., Ahn, YY. et al. Addressing the harms of AI-generated inauthentic content. Nat Mach Intell 5, 679–680 (2023). https://doi.org/10.1038/s42256-023-00690-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-023-00690-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics