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.

翻译：在使用生成式人工智能（AI）时，用户可能会看到生产力提升，但AI生成的内容可能无法完美契合其偏好。为研究这一效应，我们引入了一个贝叶斯框架，其中异质性用户需权衡输出保真度与通信成本，选择与AI共享多少信息。我们证明，这些个体层面的决策与AI训练之间的相互作用可能引发社会挑战：输出内容可能趋于同质化，尤其是当AI基于AI生成内容进行训练时；而任何AI偏见都可能演变为社会偏见。解决同质化与偏见问题的一个方案是改善人机交互，实现在不牺牲生产力的前提下提供个性化输出。