AI governance versus model management: What’s the difference?

The world of artificial intelligence (AI) is chock-full with so many buzzwords and nomenclature that it’s hard to keep track of what’s really been said. It also causes a lot of confusion for those that aren’t steeped in this world on a regular basis, and even for those that are, there’s often disagreement on terms or phrases. Case in point — AI governance and AI model management. Both seem pretty straight forward, but there’s a great amount of confusion between the two, so it’s time to set the record straight.

Why do people get these terms mixed up?

A major factor in the confusion lies in not understanding the three main different approaches to AI governance. The first approach, and the one that isn’t a culprit of misunderstanding, is the compliance-centric approach. This approach, led by Legal and Risk teams, aims to ensure AI compliance with laws and regulations by fully documenting and auditing the use of AI. This approach, while certainly important, only scratches the surface of an AI use case —  AI compliance is only one dimension of an AI project.

Read more of the blog here.