AI risk assessment frameworks

1. AI risk assessment frameworks

In the last video of this chapter on AI risk management fundamentals we'll talk about risk assessment frameworks.

2. Formal risk assessments

We'll look at what risk assessments include, why they are important, and how they can provide guidance on structuring day-to-day risk management activities. Managing AI models requires a two-pronged approach toward risk. Formal, regular risk assessments must be combined with ongoing internal checks and balances. Let's first look at formal risk assessments in more detail.

3. What are the benefits?

Formal risk assessments are essential for several reasons. First, they establish a baseline understanding of the potential risks involved with the AI system. By involving diverse stakeholders, companies gain a more comprehensive perspective on these risks. Regular assessments also allow for adaptation of the AI system and its environment as circumstances change. This helps ensure the AI functions as intended, producing safe and reliable outputs. Finally, risk assessments help with legal and regulatory compliance, minimizing the risk of losses, penalties, and reputational damage.

4. Elements of a risk assessment

A comprehensive AI risk assessment has several elements. First, actors identify possible risks like privacy issues, biases, and security vulnerabilities. Then, they analyze how likely these risks are and how strong an impact they could have. Next, they evaluate risks based on how serious they are and the company's tolerance for risk. Lastly, they develop a strategy to manage, reduce, or eliminate the risks. Let's look at the role of formal risk assessments incorporated into the EU AI Act.

5. EU AI Act

In the EU AI Act adopted in March 2024, risk assessment is a cornerstone of regulating AI development and deployment. It gives clear guidance on when formal risk assessments are necessary. AI systems are first put into groups based on how risky they are. These groups are unacceptable risk, high risk, limited risk, and minimal risk. Companies apply specific risk mitigation strategies according to this classification. For high-risk AI systems, it requires a risk assessment and risk management system. For low-risk AI systems, it allows for alternative measures that could include following relevant codes of conduct.

6. Internal checks and balances

Now let's consider the other side of the coin, internal checks and balances. These provide continuous monitoring to catch emerging risks or unexpected model behavior. Ongoing checks are set based on the results of the formal risk assessment. One example of this concept is regular bias testing to identify unfair model outputs. A second is monitoring for unexpected model behavior to catch performance degradation. These ongoing checks act as an early warning system. Potential issues can be identified and addressed before they escalate into major problems. This fosters a safety-conscious culture and ensures proactive risk management as the AI system operates in the real world.

7. This isn't just about ticking boxes

Formal risk assessments provide a path for responsible AI development, while internal checks and balances act as the guardrails that keep you on the right track. This isn't just about ticking boxes. Effective AI risk management is a culture of continuous vigilance supported by formal controls. This holistic approach ensures that AI systems comply with current regulations, such as the EU AI Act. It helps them be adaptable to emerging challenges and unforeseen requirements. By combining formal risk assessments with day-to-day monitoring, organizations can take a crucial step toward building and maintaining resilient, innovative, and trustworthy AI applications.

8. Let's practice!

Now let's do some exercises to further practice these concepts!

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.