Ensuring AI Safety

Ensuring AI Safety: The Need for Thoughtful Legislation

Artificial Intelligence (AI) has been heralded as a transformative force in various sectors, from healthcare and finance to education and entertainment. However, the rapid development of AI technologies brings with it significant safety concerns. As Arvind Narayanan and Sayash Kapoor astutely pointed out, "Safety is a property of applications, not a property of technologies (or models)." This perspective is crucial when considering legislation like California's SB-1047, which aims to regulate AI but may miss the mark in its current form.

Understanding AI Safety

To comprehend AI safety, it's essential to draw an analogy. Just as the safety of a blender cannot be judged by its motor alone, the safety of an AI system cannot be determined solely by its technology. The application and context in which AI is used are crucial for its safety.

For example, in healthcare, AI must be rigorously tested and monitored to ensure accurate diagnoses, as errors can have serious consequences. Meanwhile, AI for recommending movies needs to protect user privacy and avoid harmful content, though it may not require the same level of scrutiny.

AI's adaptability adds complexity. Models can be repurposed unpredictably, leading to potential misuse if not properly controlled. For instance, a language model for writing could generate misleading information if misused.

As AI evolves, new risks emerge, necessitating continuous evaluation and adaptation of safety protocols. This requires collaboration among developers, regulators, and users to effectively identify and mitigate risks.

AI safety involves more than just technology. It requires considering the application context, implementing safeguards, and staying vigilant as technology changes. A holistic approach ensures we can benefit from AI while minimizing risks.

The Misguided Approach of California’s SB-1047

SB-1047 fails to recognize this distinction, potentially stifling innovation while not effectively addressing the core issues of AI safety. The bill does not account for the vast number of beneficial uses of AI models, similar to how electric motors power a wide range of devices, most of which are beneficial. Unfortunately, just as it's impossible to create a motor that cannot be misused, it's also challenging to design an AI model immune to harmful adaptations.

This legislative shortcoming is particularly concerning given California's prominent role in AI innovation. California State has been a hub for technological advancements, and legislation that hampers innovation could have far-reaching consequences. Additionally, other jurisdictions often look to California as a model, which means the impact of SB-1047 could extend well beyond state lines.

The Challenges of Ensuring AI Safety

A significant challenge in AI safety is "jailbreaking," where even rigorously aligned, closed-source models can be manipulated to produce harmful responses. Recent data illustrates this concern effectively.

For example, Table 1 (below) shows the fraction of jailbreaks achieved using different methods across both open-source and closed-source models. The TAP method achieved jailbreaks in 98% of cases for the open-source model Vicuna and up to 98% for the closed-source model PaLM-2, demonstrating the vulnerability of these systems. The number of queries required to achieve these jailbreaks was relatively low, indicating the ease with which these models can be exploited.

Such exploits are frequently highlighted by "Pliny the Prompter" on social media, underscoring the persistence and ease of these vulnerabilities. Furthermore, research by Anthropic's Cem Anil and collaborators shows that "many-shot jailbreaking" can coerce leading large language models into giving inappropriate responses, posing a difficult-to-counter threat. This data highlights the need for more robust safety measures and continuous monitoring to mitigate these risks effectively.

Open Source Models and Fine-Tuning

Open-source AI models, in particular, present unique challenges. There's currently no known method to prevent fine-tuning from removing alignment achieved through Reinforcement Learning from Human Feedback (RLHF). This makes it nearly impossible to ensure that open-source models cannot be adapted for harmful purposes. The flexibility that makes these models valuable for innovation and development also makes them vulnerable to misuse.

General Guidelines and the Path Forward

LLMs have the potential to be transformational in business. Appropriate safeguards to secure models and AI-powered applications can accelerate responsible adoption and reduce risk to companies and users alike. As a significant advancement in the field, TAP not only exposes vulnerabilities but also emphasizes the ongoing need to improve security measures.

Enterprises must adopt a model-agnostic approach that can validate inputs and outputs in real-time, informed by the latest adversarial machine learning techniques. This approach ensures robust security and mitigates potential risks. Contact us to learn more about our AI Firewall and see the full research paper for additional details on TAP.

Given these complexities, how should we approach AI safety? The answer lies in nuanced, well-informed legislation that recognizes the specific challenges of AI applications rather than imposing blanket regulations on the technology itself.

The world needs a framework that encourages innovation while ensuring that AI applications are safe. This could involve:

• Developing Robust AI Governance: Establishing clear guidelines for the ethical use of AI, focusing on applications rather than the underlying technology.

• Promoting Transparency: Encouraging companies to be transparent about their AI models, including their capabilities and limitations.

• Enhancing Collaboration: Fostering collaboration between government, industry, and academia to develop best practices for AI safety.

• Investing in Research: Supporting research into AI safety, including methods to prevent jailbreaking and other exploits.

AI safety is a multifaceted issue that requires a nuanced approach. While well-intentioned regulations can sometimes risk stifling innovation without addressing the real challenges of AI safety, a more effective strategy involves focusing on the applications of AI. By fostering a collaborative, transparent, and research-driven approach, we can ensure that AI continues to benefit society while minimizing its risks. As we navigate this complex landscape, it is imperative to advocate for policies that balance innovation with safety, ensuring a future where AI can thrive responsibly.

About the Author

Sam Obeidat is an author, futurist, serial entrepreneur, an internationally recognized expert in AI strategy, and a technology product lead. He excels in developing advanced AI technologies across a variety of sectors, including education, fintech, government, defense, and healthcare.

Sam is the founder and managing partner of World AI University (WAIU), which is dedicated to preparing professionals for AI-led advancements in their respective fields. At WAIU, Sam has been instrumental in developing AI strategies for more than 30 leading organizations and spearheads the integration of diverse AI technologies. Sam is also the founder of GeminaiX, a technology that aims to automatically build digital AI replicas of human professionals with a vision where humans and machines can coexist in harmony.

Sam holds degrees in Applied Sciences and a Master of Global Management (MGM) with a focus on deep learning in investment management. He is currently pursuing a doctorate at Royal Roads University in Canada, researching the factors that drive successful AI adoption in organizations.

Connect with Sam Obeidat on LinkedIn

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