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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

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GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

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