Web Crawler Restrictions, AI Training Datasets \& Political Biases
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911201817001984 |
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| author | Bouchaud, Paul Ramaciotti, Pedro |
| author_facet | Bouchaud, Paul Ramaciotti, Pedro |
| contents | Large language models rely on web-scraped text for training; concurrently, content creators are increasingly blocking AI crawlers to retain control over their data. We analyze crawler restrictions across the top one million most-visited websites since 2023 and examine their potential downstream effects on training data composition. Our analysis reveals growing restrictions, with blocking patterns varying by website popularity and content type. A quarter of the top thousand websites restrict AI crawlers, decreasing to one-tenth across the broader top million. Content type matters significantly: 34.2% of news outlets disallow OpenAI's GPTBot, rising to 55% for outlets with high factual reporting. Additionally, outlets with neutral political positions impose the strongest restrictions (58%), whereas hyperpartisan websites and those with low factual reporting impose fewer restrictions -only 4.1% of right-leaning outlets block access to OpenAI. Our findings suggest that heterogeneous blocking patterns may skew training datasets toward low-quality or polarized content, potentially affecting the capabilities of models served by prominent AI-as-a-Service providers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09031 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Web Crawler Restrictions, AI Training Datasets \& Political Biases Bouchaud, Paul Ramaciotti, Pedro Social and Information Networks Large language models rely on web-scraped text for training; concurrently, content creators are increasingly blocking AI crawlers to retain control over their data. We analyze crawler restrictions across the top one million most-visited websites since 2023 and examine their potential downstream effects on training data composition. Our analysis reveals growing restrictions, with blocking patterns varying by website popularity and content type. A quarter of the top thousand websites restrict AI crawlers, decreasing to one-tenth across the broader top million. Content type matters significantly: 34.2% of news outlets disallow OpenAI's GPTBot, rising to 55% for outlets with high factual reporting. Additionally, outlets with neutral political positions impose the strongest restrictions (58%), whereas hyperpartisan websites and those with low factual reporting impose fewer restrictions -only 4.1% of right-leaning outlets block access to OpenAI. Our findings suggest that heterogeneous blocking patterns may skew training datasets toward low-quality or polarized content, potentially affecting the capabilities of models served by prominent AI-as-a-Service providers. |
| title | Web Crawler Restrictions, AI Training Datasets \& Political Biases |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2510.09031 |