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Human-AI Interactions and Societal Pitfalls

Author

Listed:
  • Francisco Castro
  • Jian Gao
  • S'ebastien Martin

Abstract

When working with generative artificial intelligence (AI), users may see productivity gains, but the AI-generated content may not match their preferences exactly. To study this effect, we introduce a Bayesian framework in which heterogeneous users choose how much information to share with the AI, facing a trade-off between output fidelity and communication cost. We show that the interplay between these individual-level decisions and AI training may lead to societal challenges. Outputs may become more homogenized, especially when the AI is trained on AI-generated content, potentially triggering a homogenization death spiral. And any AI bias may propagate to become societal bias. A solution to the homogenization and bias issues is to reduce human-AI interaction frictions and enable users to flexibly share information, leading to personalized outputs without sacrificing productivity.

Suggested Citation

  • Francisco Castro & Jian Gao & S'ebastien Martin, 2023. "Human-AI Interactions and Societal Pitfalls," Papers 2309.10448, arXiv.org, revised Jul 2025.
    • Handle: RePEc:arx:papers:2309.10448
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    References listed on IDEAS

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