Summary of Assurance Of Ai Systems From a Dependability Perspective, by Robin Bloomfield and John Rushby
Assurance of AI Systems From a Dependability Perspective
by Robin Bloomfield, John Rushby
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 paper outlines fundamental principles for ensuring the reliability of computer-based systems that pose substantial risks. By applying these principles to systems incorporating Artificial Intelligence (AI) and Machine Learning (ML), the study aims to address potential vulnerabilities and provide a framework for developing more trustworthy AI/ML-powered systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the way we live and work, but they also pose significant risks. A new paper explores ways to make these powerful technologies more reliable by applying classic principles of assurance. The goal is to create safer AI/ML systems that can be trusted to make good decisions. |
Keywords
» Artificial intelligence » Machine learning
