mlsanityData to 14 Oct 2026

The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandates

arXiv 2505.11577 ↗First posted 16 May 2025cs.CY cs.AIcited by 7

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Abstract (arXiv)

Recent application programming interface (API) restrictions on major social media platforms challenge compliance with the EU Digital Services Act [20], which mandates data access for algorithmic transparency. We develop a structured audit framework to assess the growing misalignment between regulatory requirements and platform implementations. Our comparative analysis of X/Twitter, Reddit, TikTok, and Meta identifies critical ``audit blind-spots'' where platform content moderation and algorithmic amplification remain inaccessible to independent verification. Our findings reveal an ``accountability paradox'': as platforms increasingly rely on AI systems, they simultaneously restrict the capacity for independent oversight. We propose targeted policy interventions aligned with the AI Risk Management Framework of the National Institute of Standards and Technology [80], emphasizing federated access models and enhanced regulatory enforcement.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 24 of its 121 references that are in the map. First posted on arXiv 16 May 2025.

Builds on

24 of 121 references.

References · 24

  1. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  2. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
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  7. 2023A Survey of Reinforcement Learning from Human Feedbackcited by 264
  8. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 126
  9. 2019Scalable and Generalizable Social Bot Detection through Data Selectioncited by 59
  10. 2024A Safe Harbor for AI Evaluation and Red Teamingcited by 47
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  12. 2021Algorithmic Amplification of Politics on Twittercited by 38
12 more
  1. 2023Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Sciencecited by 37
  2. 2022Botometer 101: Social bot practicum for computational social scientistscited by 25
  3. 2023The Dark Side of ChatGPT: Legal and Ethical Challenges from Stochastic Parrots and Hallucinationcited by 23
  4. 2024Foundation Model Transparency Reportscited by 14
  5. 2022A Transparency Index Framework for AI in Educationcited by 13
  6. 2023Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?cited by 10
  7. 2024Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil societycited by 7
  8. 2022Geo-Information Harvesting from Social Media Datacited by 5
  9. 2022Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interestcited by 4
  10. 2023Characteristics of ChatGPT users from Germany: implications for the digital divide from web tracking datacited by 4
  11. 2024Lower Quantity, Higher Quality: Auditing News Content and User Perceptions on Twitter/X Algorithmic versus Chronological Timelinescited by 2
  12. 2020Signs for Ethical AI: A Route Towards Transparencycited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 6.

7 papers.