Summary of Coordinated Flaw Disclosure For Ai: Beyond Security Vulnerabilities, by Sven Cattell et al.
Coordinated Flaw Disclosure for AI: Beyond Security Vulnerabilities
by Sven Cattell, Avijit Ghosh, Lucie-Aimée Kaffee
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Coordinated Flaw Disclosure (CFD) framework addresses the lack of a structured process for disclosing and addressing algorithmic flaws in Artificial Intelligence (AI). The framework is tailored to the complexities of machine learning (ML) models, building on emerging participatory auditing methods and cybersecurity norms. Key innovations include extended model cards, dynamic scope expansion, an independent adjudication panel, and automated verification processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Harm reporting in AI currently lacks a structured process for disclosing and addressing algorithmic flaws. A new framework is needed to address this gap. The proposed Coordinated Flaw Disclosure (CFD) framework is designed specifically for the complexities of ML models. It’s based on emerging participatory auditing methods and cybersecurity norms. This framework could help improve public trust in AI systems. |
Keywords
» Artificial intelligence » Machine learning
