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

arXiv: https://arxiv.org/abs/2407.01557 · HTML: https://mlsanity.com/paper/2407.01557/

## Key facts

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

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://mlsanity.com/paper/2110.08193/) (2021)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [R-Judge: Benchmarking Safety Risk Awareness for LLM Agents](https://mlsanity.com/paper/2401.10019/) (2024)
- [The Foundation Model Transparency Index](https://mlsanity.com/paper/2310.12941/) (2023)
- [KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge](https://mlsanity.com/paper/2402.13605/) (2024)
- [HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild](https://mlsanity.com/paper/2403.04307/) (2024)
- [CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models](https://mlsanity.com/paper/2306.16244/) (2023)

## Built on by (most cited first, 14 of 14)

- [Agentic Web: Weaving the Next Web with AI Agents](https://mlsanity.com/paper/2507.21206/) (2025)
- [Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models](https://mlsanity.com/paper/2407.13934/) (2024)
- [SafeLawBench: Towards Safe Alignment of Large Language Models](https://mlsanity.com/paper/2506.06636/) (2025)
- [Reasoning-Enhanced Large Language Models for Molecular Property Prediction](https://mlsanity.com/paper/2510.10248/) (2025)
- [Facilitating Long Context Understanding via Supervised Chain-of-Thought Reasoning](https://mlsanity.com/paper/2502.13127/) (2025)
- [Beyond No: Quantifying AI Over-Refusal and Emotional Attachment Boundaries](https://mlsanity.com/paper/2502.14975/) (2025)
- [Unlocking the Latent Canvas: Eliciting and Benchmarking Symbolic Visual Expression in LLMs](https://mlsanity.com/paper/2603.14505/) (2026)
- [Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA](https://mlsanity.com/paper/2605.14928/) (2026)
- [Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps](https://mlsanity.com/paper/2505.18426/) (2025)
- [On Integrating Large Language Models and Scenario-Based Programming for Improving Software Reliability](https://mlsanity.com/paper/2509.09194/) (2025)
- [CogniAlign: Survivability-Grounded Multi-Agent Moral Reasoning for Safe and Transparent AI](https://mlsanity.com/paper/2509.13356/) (2025)
- [Bridging Language Gaps with Adaptive RAG: Improving Indonesian Language Question Answering](https://mlsanity.com/paper/2510.21068/) (2025)
- [CodegenBench: Can LLMs Write Efficient Code Across Architectures?](https://mlsanity.com/paper/2606.04023/) (2026)
- [TextGaze: Prompting Gaze Target Estimation with Textual Scene Cues](https://mlsanity.com/paper/2607.10130/) (2026)

## 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.