mlsanityData to 14 Oct 2026

An Empirical Study on Compliance with Ranking Transparency in the Software Documentation of EU Online Platforms

arXiv 2312.14794 ↗First posted 22 Dec 2023cs.SE cs.AIcited by 2

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Abstract (arXiv)

Compliance with the European Union's Platform-to-Business (P2B) Regulation is challenging for online platforms, and assessing their compliance can be difficult for public authorities. This is partly due to the lack of automated tools for assessing the information (e.g., software documentation) platforms provide concerning ranking transparency. Our study tackles this issue in two ways. First, we empirically evaluate the compliance of six major platforms (Amazon, Bing, Booking, Google, Tripadvisor, and Yahoo), revealing substantial differences in their documentation. Second, we introduce and test automated compliance assessment tools based on ChatGPT and information retrieval technology. These tools are evaluated against human judgments, showing promising results as reliable proxies for compliance assessments. Our findings could help enhance regulatory compliance and align with the United Nations Sustainable Development Goal 10.3, which seeks to reduce inequality, including business disparities, on these platforms.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 54 references that are in the map. First posted on arXiv 22 Dec 2023.

Builds on

6 of 54 references.

References · 6

  1. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  2. 2015A large annotated corpus for learning natural language inferencecited by 1,847
  3. 2023Can ChatGPT Assess Human Personalities? A General Evaluation Frameworkcited by 70
  4. 2021An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainabilitycited by 16
  5. 2023Distilling ChatGPT for Explainable Automated Student Answer Assessmentcited by 14
  6. 2022Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyondcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.