AI needs guardrails as generative AI runs rampant

What is AI governance?

AI governance is more than detecting bias, ensuring fairness or providing explainability. It's a framework and set of policies and processes that ensures AI is researched, developed and utilized properly. It's about ensuring AI technologies are deployed responsibly, ethically and transparently while taking into consideration potential risks, privacy and security.

I would argue the most important aspect of establishing effective AI governance is people. There needs to be a high level of collaboration between policymakers, data science experts, researchers and stakeholders in an organization. This is really the only way organizations can foster the level of trust, fairness and accountability required to ensure proper use of AI without jeopardizing security, compliance and societal values.

The significance of guardrails

While AI presents numerous opportunities as organizations haphazardly look to implement it, the potential risks of improper use must be carefully managed to avoid unintended consequences. With many organizations just now embarking on their AI journeys rooted in the desire to use large language models (LLMs), the risks are high. It's a big reason why we've seen leading AI vendors prioritize the simplification of consumption through open source wrapper solutions to ramp up usage quickly and confidently.

The next wave from these leading vendors will be focused on guardrails. Think of AI guardrails as safety mechanisms, providing guidelines and limits to ensure that AI systems are developed and utilized in a manner that aligns with ethical standards and societal expectations. A great example can be seen from Nvidia's recent announcement of NeMo Guardrails, which aims to ensure that in-house applications powered by LLMs are accurate, appropriate and secure. The software provides organizations with sample code, examples and full documentation to deliver organizations the transparency necessary to safely develop and deploy generative AI applications.

A framework for delivering responsible AI

For AI systems to be viewed as trustworthy, several criteria must be met to reduce the likelihood of risks occurring. This criteria includes seven topic areas that, when appropriately prioritized and balanced, deliver the highest level of responsible AI today. At the same time, neglecting even one can significantly increase the chance of a bad outcome. They include the following:

1. Transparency and accountability. To promote trust, enable understanding of decision-making and hold individuals and organizations responsible for the outcomes of AI systems, organizations must understand the inner workings of AI systems, as well as establishing clear responsibilities shared among all those involved with defined levels of oversight to offset misuse.
2. Accuracy and reliability. Unreliable or poorly designed generalized AI systems increase risk and diminish trust. Organizations must be able to confirm that a system continually meets requirements based on its intended use and that results align to expected values. Continuous testing or monitoring that verifies a system is operating as planned is frequently used to evaluate the validity and dependability of deployed AI systems.
3. Security and resiliency. While systems must be able to avoid and protect against attacks, there should also be assurances in place that detail corrective action when recovery is necessary.
4. Explainability. Organizations must understand how AI has arrived at a certain conclusion. Further, the conclusion should be interpretable to users by providing references and justifications as to why a specific outcome was achieved.
5. Bias and fairness. Carefully consider all aspects of data used to influence AI outcomes, including data collection, experimentation, algorithm design and ongoing monitoring. Organizations should look for ways to prevent discriminatory outcomes and unequal treatment based on existing societal biases and focus on ensuring AI systems are fair across different demographic groups.
6. Privacy. AI systems should maintain compliance by protecting the privacy and security of data used throughout the AI lifecycle, including research, training and inference. That means continued adherence to data protection regulations, anonymization, confidentiality and encryption should all be in place to safeguard sensitive data.
7. Safety. Ensuring AI systems do not cause physical harm is the ultimate table stake. To ensure these implementations don't become dangerous, organizations should put their systems under ongoing simulations and testing, while also enabling kill switches and human intervention when unexpected functionality or outcomes occur.

Maximizing the potential of AI

AI governance and the implementation of AI guardrails are essential for maximizing the potential of AI while mitigating risks. Organizations must be able to explain what, why and how AI risks occur. However, so many organizations struggle to understand where to get started. They need to focus on areas such as ethical guidelines, risk management, transparency and governance. They should seek out partners that can provide valuable guidance on establishing a solid foundation for responsible AI development and deployment.