Trustworthy, Responsible, and Safe AI: A Comprehensive Architectural Framework for AI Safety with Challenges and Mitigations

• URL: http://arxiv.org/abs/2408.12935v2
• Date: Thu, 12 Sep 2024 07:45:50 GMT
• Title: Trustworthy, Responsible, and Safe AI: A Comprehensive Architectural Framework for AI Safety with Challenges and Mitigations
• Authors: Chen Chen, Ziyao Liu, Weifeng Jiang, Si Qi Goh, Kwok-Yan Lam,
• Abstract summary: AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems. Our goal is to promote advancement in AI safety research, and ultimately enhance people's trust in digital transformation.
• Score: 14.150792596344674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems. With the rapid proliferation of AI and especially with the recent advancement of Generative AI (or GAI), the technology ecosystem behind the design, development, adoption, and deployment of AI systems has drastically changed, broadening the scope of AI Safety to address impacts on public safety and national security. In this paper, we propose a novel architectural framework for understanding and analyzing AI Safety; defining its characteristics from three perspectives: Trustworthy AI, Responsible AI, and Safe AI. We provide an extensive review of current research and advancements in AI safety from these perspectives, highlighting their key challenges and mitigation approaches. Through examples from state-of-the-art technologies, particularly Large Language Models (LLMs), we present innovative mechanism, methodologies, and techniques for designing and testing AI safety. Our goal is to promote advancement in AI safety research, and ultimately enhance people's trust in digital transformation.

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