What's documented in AI? Systematic Analysis of 32K AI Model Cards
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909098780393472 |
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| author | Liang, Weixin Rajani, Nazneen Yang, Xinyu Ozoani, Ezinwanne Wu, Eric Chen, Yiqun Smith, Daniel Scott Zou, James |
| author_facet | Liang, Weixin Rajani, Nazneen Yang, Xinyu Ozoani, Ezinwanne Wu, Eric Chen, Yiqun Smith, Daniel Scott Zou, James |
| contents | The rapid proliferation of AI models has underscored the importance of thorough documentation, as it enables users to understand, trust, and effectively utilize these models in various applications. Although developers are encouraged to produce model cards, it's not clear how much information or what information these cards contain. In this study, we conduct a comprehensive analysis of 32,111 AI model documentations on Hugging Face, a leading platform for distributing and deploying AI models. Our investigation sheds light on the prevailing model card documentation practices. Most of the AI models with substantial downloads provide model cards, though the cards have uneven informativeness. We find that sections addressing environmental impact, limitations, and evaluation exhibit the lowest filled-out rates, while the training section is the most consistently filled-out. We analyze the content of each section to characterize practitioners' priorities. Interestingly, there are substantial discussions of data, sometimes with equal or even greater emphasis than the model itself. To evaluate the impact of model cards, we conducted an intervention study by adding detailed model cards to 42 popular models which had no or sparse model cards previously. We find that adding model cards is moderately correlated with an increase weekly download rates. Our study opens up a new perspective for analyzing community norms and practices for model documentation through large-scale data science and linguistics analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05160 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What's documented in AI? Systematic Analysis of 32K AI Model Cards Liang, Weixin Rajani, Nazneen Yang, Xinyu Ozoani, Ezinwanne Wu, Eric Chen, Yiqun Smith, Daniel Scott Zou, James Software Engineering Artificial Intelligence Machine Learning The rapid proliferation of AI models has underscored the importance of thorough documentation, as it enables users to understand, trust, and effectively utilize these models in various applications. Although developers are encouraged to produce model cards, it's not clear how much information or what information these cards contain. In this study, we conduct a comprehensive analysis of 32,111 AI model documentations on Hugging Face, a leading platform for distributing and deploying AI models. Our investigation sheds light on the prevailing model card documentation practices. Most of the AI models with substantial downloads provide model cards, though the cards have uneven informativeness. We find that sections addressing environmental impact, limitations, and evaluation exhibit the lowest filled-out rates, while the training section is the most consistently filled-out. We analyze the content of each section to characterize practitioners' priorities. Interestingly, there are substantial discussions of data, sometimes with equal or even greater emphasis than the model itself. To evaluate the impact of model cards, we conducted an intervention study by adding detailed model cards to 42 popular models which had no or sparse model cards previously. We find that adding model cards is moderately correlated with an increase weekly download rates. Our study opens up a new perspective for analyzing community norms and practices for model documentation through large-scale data science and linguistics analysis. |
| title | What's documented in AI? Systematic Analysis of 32K AI Model Cards |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.05160 |