Summary of Reliability, Resilience and Human Factors Engineering For Trustworthy Ai Systems, by Saurabh Mishra et al.
Reliability, Resilience and Human Factors Engineering for Trustworthy AI Systems
by Saurabh Mishra, Anand Rao, Ramayya Krishnan, Bilal Ayyub, Amin Aria, Enrico Zio
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Systems and Control (eess.SY)
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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 This paper proposes an innovative framework that integrates traditional reliability and resilience engineering principles into artificial intelligence (AI) systems. The authors develop a comprehensive approach that combines metrics such as failure rate and Mean Time Between Failures (MTBF) with resilience engineering and human reliability analysis to manage AI system performance, prevent failures, or efficiently recover from them. The proposed framework is applied to a real-world AI system using data from openAI platforms, demonstrating its practical applicability. The authors’ aim is to guide policy, regulation, and the development of reliable, safe, and adaptable AI technologies capable of consistent performance in real-world environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) systems are becoming more important in many areas. But we need to make sure they work well and don’t fail often. This paper shows how to use old ideas from engineering to make AI systems better. It combines different ways of measuring reliability, like how often things go wrong and how long it takes for something to happen again. The authors tested their idea on a real AI system and showed that it works. They want to help create better AI systems that work well in the real world. |
