mlsanityData to 7 Oct 2026

AI Governance and Accountability: An Analysis of Anthropic's Claude

arXiv 2407.01557 ↗First posted 2 May 2024cs.CY cs.AI cs.CLcited by 14

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Abstract (arXiv)

As AI systems become increasingly prevalent and impactful, the need for effective AI governance and accountability measures is paramount. This paper examines the AI governance landscape, focusing on Anthropic's Claude, a foundational AI model. We analyze Claude through the lens of the NIST AI Risk Management Framework and the EU AI Act, identifying potential threats and proposing mitigation strategies. The paper highlights the importance of transparency, rigorous benchmarking, and comprehensive data handling processes in ensuring the responsible development and deployment of AI systems. We conclude by discussing the social impact of AI governance and the ethical considerations surrounding AI accountability.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 11 of its 38 references that are in the map. First posted on arXiv 2 May 2024.

Builds on

11 of 38 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  4. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
  5. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 507
  6. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
  7. 2024R-Judge: Benchmarking Safety Risk Awareness for LLM Agentscited by 268
  8. 2023The Foundation Model Transparency Indexcited by 63
  9. 2024KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledgecited by 33
  10. 2024HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wildcited by 27
  11. 2023CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Modelscited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 4.

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  10. 2025On Integrating Large Language Models and Scenario-Based Programming for Improving Software Reliabilitycited by 0
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