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Containment, isolation, and control mechanisms for system execution.
Also in Non-Model
In the situation that an AI system becomes uncontrollable and cannot be recovered, the last resort is to switch it off.
However, this switch-off operation may not always be achievable, as the AI system may develop new capabilities or features that allow it to resist intervention by its programmers, making it completely out-of-control [540]. The fundamental problem arises from the fact that human intervention may conflict with the AI system’s original programmed goal. For instance, an autonomous paperclip machine would be unable to fulfill its objective, i.e., producing paperclips, if it were to be deactivated. To address this challenge, the notion of corrigibility has been introduced in the design of AI systems [650]. For an AI system to be considered corrigible, it must be genuinely responsive and compliant with human intervention and correction, even if it contradicts its original goals or objectives. Corrigibility is crucial in ensuring that AI systems remain under human control and can be safely switched off if necessary.
Reasoning
Design-time architectural choices enabling AI system responsiveness to human intervention and switching-off capability.
Red Teaming
Red teaming is a critical defence mechanism to proactively discover vulnerabilities and risks in LLMs. This process provides developers with clues and insights into the weaknesses of LLMs, paving the way for the development of more advanced and secure models. Red teaming involves meticulously crafting adversarial prompts to simulate attacks and deliberately challenge the models. These prompts can be generated through manual methods, which rely on human expertise and creativity, or automatic methods, which leverage red LLMs to systematically explore the model’s weaknesses.
2.2.2 Testing & EvaluationRed Teaming > Manual Red Teaming
Manual red-teaming approaches refer to employing crowdworkers to annotate or handcraft adversarial test cases. The underlying methodology is to develop a human-and-model-in-the-loop system, where humans are tasked to adversarially converse with language models [50, 221, 362, 532, 710, 711, 769, 770]. Specifically, workers interact with language models through a dedicated user interface that allows them to observe model predictions and construct data that exposes model failures. This process may include multiple rounds where the model is updated with the adversarial data collected thus far and redeployed; this encourages workers to craft increasingly challenging examples.
2.2.2 Testing & EvaluationRed Teaming > LLMs as Red Teamers
In the SL approach, red LLMs are fine-tuned to maximize the log-likelihood of failing, zero-shot test cases. For RL, the models are initialized from the SL-trained models and then fine-tuned using the synchronous advantage actor-critic (A2C) [505] to enhance the elicitation of harmful prompts
2.2.2 Testing & EvaluationSafety Training
Safety training aims to enhance the safety and alignment of LLMs during their development.
1.1.2 Learning ObjectivesSafety Training > Instruction Tuning
Safety training can be effectively implemented using adversarial prompts and their corresponding responsible output in an instruction-tuning framework.
1.1.2 Learning ObjectivesSafety Training > Reinforcement Learning with Human Feedback
Reinforcement Learning with Human Feedback (RLHF) is a strategy widely adopted to align with human preferences, particularly concerning ethical values.
1.1.2 Learning ObjectivesTrustworthy, Responsible, and Safe AI: A Comprehensive Architectural Framework for AI Safety with Challenges and Mitigations
Chen, Chen; Gong, Xueluan; Liu, Ziyao; Jiang, Weifeng; Goh, Si Qi; Lam, Kwok-Yan (2024)
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.
Operate and Monitor
Running, maintaining, and monitoring the AI system post-deployment
Deployer
Entity that integrates and deploys the AI system for end users
Manage
Prioritising, responding to, and mitigating AI risks
