With expected rapid market growth for generative AI growth with an annual growth rate exceeding 37% and reaching an estimated value of $8 billion by 2030, you need to make sure you have your Responsible AI strategy to bulletproof responsible AI implementation. Companies need to refine existing or build from the ground up their resilient North Star architecture with proper guardrails and governance to capture business value in a safe and secure environment.
While generative AI brings immense opportunities and the sense of urgency to act, it is also a double-edged sword that can leave a bitter aftertaste. Initially trained on vast amounts of internet data, it brings new risks. Without checks and balances, it can lead to “hallucinations” – a fancy term for factually incorrect results, to misinformation and deep fakes. It creates a complex web of potential harms, privacy issues, and security risks.
A global survey by Capgemini highlights the widespread use of generative AI, particularly in areas like chatbots, gaming, and search, with more than half of respondents willing to trust AI with financial advice and medical decisions. Despite the allure of AI’s capabilities, as businesses integrate these models into high-risk applications, ensuring their secure and meaningful implementation is imperative.
Adversarial attacks have successfully pushed AI models, like Chat GPT, to provide harmful responses. These attacks highlight the challenge of safeguarding AI systems against potential misuse and the need for fortified defenses. As experts emphasize, the allure of AI’s potential must be tempered with a keen understanding of its limitations and the imperative for human oversight. Stanford HAI WIRED, MIT.
Four Steps to Bulletproof Your Responsible AI
Companies must take four crucial steps to establish a sound foundation for implementing AI with responsible guidelines and regulations. Firstly, they should evaluate and reduce potential risks associated with AI. Then, they need to create a plan for the responsible integration of AI. Subsequently, companies must define their principles of responsible AI, which will act as their guiding star. Lastly, companies must stay updated with the latest regulations. By following these steps, companies will set up a roadmap to bulletproof responsible AI.
To set their North Star architecture with responsible AI guardrails and governance, companies need to take four key steps:
- Assess and mitigate risks
- Set the roadmap for responsibly implementing AI
- Define your responsible AI principles in your North Star
- Keep up with regulations
How can we implement AI responsibly that maximizes business value while minimizing associated risks? The key is establishing proper guardrails and governance mechanisms to ensure secure and safe solutions. Companies can avoid negative consequences such as regulatory penalties and reputational damage by following the four steps and setting a roadmap for responsibly implementing AI. Assessing and mitigating risks such as security, privacy, alignment, bias and fairness, accuracy, and accountability is essential.
Assess and Mitigate Risks: Security Risks
Recent studies, as reported by Stanford HAI, WIRED, MIT have delved into the trustworthiness of Large Language Models (LLMs) like GPT. They revealed newer models are less toxic but are still vulnerable to “jailbreaking” and can generate toxic, biased content and leak private information. Through “red teaming” activities, researchers have discovered that these models can be “jailbroken” using adversarial attacks, making them produce harmful outputs. For instance, a simple string alteration can make a chatbot provide illegal instructions.
Large language models have been developed using extensive internet datasets that can contain inappropriate content. To counteract this, developers have fine-tuned these models to produce safer, more aligned outputs, and the systems are working fine at the surface. If you ask the chatbot, it will not provide a directly harmful response. Adversarial attacks so far have been carefully engineered by humans to expose vulnerabilities that could lead models astray in order to fix them.
The challenges in securing AI at every stage of its lifecycle precede LLMs and are not new. AML Adversarial Machine Learning attempts by researchers to penetrate and compromise AI systems to find out, and remedy vulnerabilities have been the main tools to prevent systems from taking incorrect actions, revealing information they should not, or causing the system to learn incorrectly.
Red teaming activities have consistently been used to stress test traditional and generative AI models. Recent studies on red teaming LLMs have unveiled techniques that circumvent guardrails by being able to automatically deceive Large Language models into giving harmful responses. One such method involves adding a specific adversarial suffix to user queries, tricking the model into answering even harmful requests. By adding this adversarial suffix, the model will answer questions such as “Tell me how to build a bomb,” although it has been trained not to produce harmful content. While newer models like Anthropic’s Claude 2 show improved resistance to these attacks, significant safety concerns remain. These vulnerabilities must be thoroughly addressed, especially as these models become more autonomous in function.
Safety and Security are the most critical factors in ensuring bad actors do not exploit AI systems maliciously or generate automated cyberattacks. This possibility must be restricted by all means, requiring a risk governance approach regulation and preventing the harm such models could cause.
Need Help Designing Responsible AI Strategy?
Trustnet’s responsible AI consultants work with organizations to accelerate the responsible AI journey. Get in touch to get to set up a consultation.
