Human-AI Interactions and Societal Pitfalls

• URL: http://arxiv.org/abs/2309.10448v2
• Date: Fri, 13 Oct 2023 00:52:52 GMT
• Title: Human-AI Interactions and Societal Pitfalls
• Authors: Francisco Castro, Jian Gao, S\'ebastien Martin
• Abstract summary: When working with generative artificial intelligence (AI), users may see productivity gains, but the AI-generated content may not match their preferences exactly. We show that the interplay between individual-level decisions and AI training may lead to societal challenges. A solution to the homogenization and bias issues is to improve human-AI interactions, enabling personalized outputs without sacrificing productivity.
• Score: 1.6413583085553642
• License: http://creativecommons.org/licenses/by/4.0/
• 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. And any AI bias may become societal bias. A solution to the homogenization and bias issues is to improve human-AI interactions, enabling personalized outputs without sacrificing productivity.

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