Summary of Trustworthy, Responsible, and Safe Ai: a Comprehensive Architectural Framework For Ai Safety with Challenges and Mitigations, by Chen Chen et al.
Trustworthy, Responsible, and Safe AI: A Comprehensive Architectural Framework for AI Safety with Challenges and Mitigations
by Chen Chen, Xueluan Gong, Ziyao Liu, Weifeng Jiang, Si Qi Goh, Kwok-Yan Lam
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 a novel architectural framework for understanding and analyzing AI Safety, addressing the growing concerns about the safe adoption and deployment of AI systems. The framework considers three perspectives: Trustworthy AI, Responsible AI, and Safe AI. By reviewing current research and advancements in AI safety from these perspectives, the authors highlight key challenges and mitigation approaches. Innovative mechanisms, methodologies, and techniques are presented for designing and testing AI safety, particularly through state-of-the-art technologies like Large Language Models (LLMs). The goal is to promote advancement in AI safety research, enhancing people’s trust in digital transformation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI Safety is a crucial area of study that ensures the safe use of artificial intelligence. This paper develops a new way to think about AI Safety by looking at it from three angles: Trustworthy AI, Responsible AI, and Safe AI. The authors look back at what has already been done in this field and identify the challenges and solutions. They also show how Large Language Models can be used to design and test AI safety. |
