Human-AI Interactions and Societal Pitfalls
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866918083875045376 |
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| author | Castro, Francisco Gao, Jian Martin, Sébastien |
| author_facet | Castro, Francisco Gao, Jian Martin, Sébastien |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2309_10448 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Human-AI Interactions and Societal Pitfalls Castro, Francisco Gao, Jian Martin, Sébastien Artificial Intelligence Human-Computer Interaction General Economics Economics 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. |
| title | Human-AI Interactions and Societal Pitfalls |
| topic | Artificial Intelligence Human-Computer Interaction General Economics Economics |
| url | https://arxiv.org/abs/2309.10448 |