← 研踪 PaperTrailarXiv:2611.16800 · v1
A Code-Level Audit of X
首次提交 2026-11-12 21:42
版本更新 2026-11-12 21:42
雷达观测 2026-11-13 14:15
摘要
The transparency regime of recommender systems on Very Large Online Platforms (VLOPs) is undergoing significant change under the EU Digital Services Act (DSA). The DSA requires platforms to disclose information about their recommendation pipelines and grants vetted researchers access to non-public data, which may extend to recommender code. However, it is not known to what extent such code releases enable substantive and meaningful third-party auditing of a production recommender system. We address this question by proposing a code-level audit methodology for recommender systems and applying it to the X platform, which has voluntarily open-sourced its recommender pipeline. Our methodology is a backwards-walk traversal of the repository, tracing each decision-relevant component from the user-facing timeline back to its upstream producers, checking that it is reachable from the served pipeline, and classifying each under a four-tier inspectability scheme. Applying this procedure, we identify 13 transparency dead-ends on the live serving path: models, services, label lists, and configuration values that the public code depends on but the release does not contain. We find that the components that decide which posts are filtered and how they are ranked fall into the two least inspectable tiers, a pattern that intensifies with the 2025 integration of the proprietary Grok language model into moderation, annotation, and ranking. We further show that the release keeps 2023 code that the 2025 pipeline no longer reaches, with 36 parameter references that would pass for dead-ends without a reachability check. Finally, we discuss which additional artifacts would be required for meaningful auditing.
作者备注：Submitted. Currently under peer-review
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v1 · 2026-11-12 21:42 · 最新观测版本
A Code-Level Audit of X
The transparency regime of recommender systems on Very Large Online Platforms (VLOPs) is undergoing significant change under the EU Digital Services Act (DSA). The DSA requires platforms to disclose information about their recommendation pipelines and grants vetted researchers access to non-public data, which may extend to recommender code. However, it is not known to what extent such code releases enable substantive and meaningful third-party auditing of a production recommender system. We address this question by proposing a code-level audit methodology for recommender systems and applying it to the X platform, which has voluntarily open-sourced its recommender pipeline. Our methodology is a backwards-walk traversal of the repository, tracing each decision-relevant component from the user-facing timeline back to its upstream producers, checking that it is reachable from the served pipeline, and classifying each under a four-tier inspectability scheme. Applying this procedure, we identify 13 transparency dead-ends on the live serving path: models, services, label lists, and configuration values that the public code depends on but the release does not contain. We find that the components that decide which posts are filtered and how they are ranked fall into the two least inspectable tiers, a pattern that intensifies with the 2025 integration of the proprietary Grok language model into moderation, annotation, and ranking. We further show that the release keeps 2023 code that the 2025 pipeline no longer reaches, with 36 parameter references that would pass for dead-ends without a reachability check. Finally, we discuss which additional artifacts would be required for meaningful auditing.
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