AI Governance

 Articles

Better Together: The difference between MLOps & AI Governance and why you need both to deliver on Responsible AI

At Credo AI, we believe that AI Governance is the missing—and often forgotten—link between MLOps and AI’s success to meet business objectives. In this blog post, we’ll start by defining MLOps and AI Governance, how they differ, and why both are needed for the successful realization of AI/ML projects. Let’s take a closer look at MLOps and AI Governance with respect to scope of work, stakeholder involvement, and development lifecycle.

Operationalizing Responsible AI: How do you “do” AI Governance?

Now that we’ve established what AI governance is and why it’s so important, let’s talk strategy; how does one do AI governance, and what does an effective AI governance program look like? At the highest level, AI governance can be broken down into four components—four distinct steps that make up both a linear and iterative process: 1) Alignment: identifying and articulating the goals of the AI system, 2) Assessment: evaluating the AI system against the aligned goals, 3) Translation: turning the outputs of assessment into meaningful insights, and 4) Mitigation: taking action to prevent failure. Let’s take a deeper look at what happens during each of these steps and how they come together to form a governance process designed to prevent catastrophic failure.

Cutting Through the Noise: What Is AI Governance and Why Should You Care?

There is a lack of consensus around what AI Governance actually entails. We’d like to cut through the noise and provide a definition of AI Governance rooted in Credo AI’s experience working with organizations across different industries and sectors, collaborating with policymakers and standard-setting bodies worldwide, and supporting various global 2000 customers to deliver responsible AI at scale.

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