Summary of Landscape Of Ai Safety Concerns — a Methodology to Support Safety Assurance For Ai-based Autonomous Systems, by Ronald Schnitzer et al.
Landscape of AI safety concerns – A methodology to support safety assurance for AI-based autonomous systems
by Ronald Schnitzer, Lennart Kilian, Simon Roessner, Konstantinos Theodorou, Sonja Zillner
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 develops a novel methodology for creating safety assurance cases for AI-based systems, focusing on mitigating AI-specific insufficiencies or “AI safety concerns.” This approach aims to thoroughly analyze and demonstrate the absence of such concerns, ensuring the safe integration of AI into modern autonomous systems. The authors propose the “Landscape of AI Safety Concerns” methodology, which is illustrated through a case study involving a driverless regional train. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial Intelligence (AI) is changing the way we live, but it’s crucial that these advancements are safe and reliable. Right now, it’s hard to be sure if AI systems will behave correctly in all situations. The problem is that there aren’t clear rules or guidelines for making AI safer. This paper suggests a new way to solve this problem by creating a special kind of map, called the “Landscape of AI Safety Concerns.” This map helps experts identify and fix potential safety issues with AI systems. A real-life example of how this works is shown using a self-driving train. |
