Navigating an abundance of AI regulatory initiatives

Navigating an abundance of AI regulatory initiatives

We are in an exciting period in which artificial intelligence (AI) is reshaping industries. As AI adoption continues to advance rapidly, governments find themselves racing to establish regulatory frameworks to control AI use and mitigate the risks associated with it. 

Global Efforts in AI Regulation

Globally, there’s a noticeable surge in efforts to craft AI-specific regulations. This has recently been illustrated by a review paper from Correa et al., discussing over 200 AI ethics guidelines. Introducing guidelines is far from straightforward, however. AI’s rapid evolution means that regulations must be both flexible and forward-looking to remain relevant. Additionally, many existing laws, particularly those concerning privacy, are also applicable to AI, creating a complex regulatory overlap.

Several European countries are actively investigating AI-related breaches. For example, the Italian privacy authority is investigating OpenAI. This effort is part of a broader trend in which overlapping initiatives, from the G7 to the United Nations, create a labyrinth of regulations for companies to navigate.

Can model risk management technology help firms to stay in control of their models and be compliant? 

Navigating a rapidly evolving and complex regulatory landscape can be a challenge. To stay compliant, firms have to investigate and leverage technologies.

Model Risk Management (MRM) tools, such as a model inventory, are widely used technologies in financial services companies. Proven to mitigate risks associated with model use, these platforms accelerate the pace at which AI algorithms can be deployed. To illustrate, when companies adhere to an MRM framework, all models have to pass a certain number of checks before they can be deployed into production. For example, to prove that a model has been properly tested, independent model validators must review whether the model is fit for purpose. These procedures can be modeled in a workflow, and users are then guided through the various steps.

Applying this to AI use cases, this means that before an AI application is put into production, several workflows must be followed and completed. While executing these workflows, MRM tools such as the Yields MRM platform can guide the developer and validator to select the appropriate use case, geography, and governance framework that will have to be applied to a model. The platform will then compute and identify which rules apply and request the necessary documentation and test results to satisfy these rules. This process ensures that all steps are accomplished and in place before deployment.

This approach also allows for the implementation of what are termed preventive controls. Only after all requirements are met and approvals are obtained does an AI model achieve the status necessary for production deployment. This approach guarantees that CI/CD pipelines only deploy models that comply with the established regulations.

Conclusion: A Future Shaped by Regulation and Compliance

As the AI landscape becomes increasingly complex with competing regulatory initiatives, the need for sophisticated compliance tools such as the Yields MRM technology is evident. The Yields MRM platform enables firms to navigate the regulatory maze, ensuring that AI’s potential is harnessed responsibly and ethically.