Pith. sign in

REVIEW 3 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.01557 v1 pith:TNU7ROQK submitted 2024-05-02 cs.CY cs.AIcs.CL

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

Aman Priyanshu , Yash Maurya , Zuofei Hong This is my paper
classification cs.CY cs.AIcs.CL
keywords governanceaccountabilityclaudeanthropicsystemsanalysisanalyzebecome
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. TextGaze: Prompting Gaze Target Estimation with Textual Scene Cues

    cs.CV 2026-07 accept novelty 6.0

    TextGaze fuses LVLM-generated textual cues of appearance, action and gaze direction with DINOv2 features via a hierarchically supervised transformer to estimate gaze heatmaps and in/out status.

  2. CodegenBench: Can LLMs Write Efficient Code Across Architectures?

    cs.SE 2026-06 unverdicted novelty 6.0

    CodegenBench shows LLMs generate optimized code well for x86_64 but exhibit significant performance degradation on Sunway and Kunpeng due to limited documentation and training data.

  3. Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA

    cs.CL 2026-05 unverdicted novelty 6.0

    Introduces ProcedureVQA benchmark and Chain-of-Procedure framework that improves VLM next-step prediction in procedures by up to 13% over baselines.