Blog of the Week: AI Governance - Why our tested framework is essential in an AI world
AI is everywhere these days. We’re thrilled by the tidal wave of interest because it perfectly aligns with our core mission. As we speed into a new AI era, there’s a critical element that’s often missing when organizations rush forward in hyper-competitive markets to build scalable, trusted AI programs — and that’s AI governance.
Every day I hear stories from CEOs who want to spin up AI programs simply to keep pace with the competition. It is very exciting to see senior executive interest, sponsorship, and urgency on the topic. Before you get too far into your AI journey, you’ll want to ensure your team is utilizing an AI governance framework, and that’s what I’ll focus on in this blog.
To a large degree, AI governance is an extension of our data governance efforts tested many times over with organizations around the world.
An AI governance framework offers a blueprint for how to create successful AI products. It is a map to a repeatable process for driving long-term, reliable AI programs.
Our AI governance framework: A proven 4-step process
If you want to ensure AI is used responsibly, an AI governance framework provides a set of principles and practices for governing its development, deployment and use. Our framework is informed by our definition of AI governance:
AI governance is the application of rules, processes and responsibilities to drive maximum value from your automated data products by ensuring applicable, streamlined and ethical AI practices that mitigate risk, adhere to legal requirements and protect privacy.
Clearly, there is a strong link between driving maximum value and effective governance; in other words, the relationship between the ROI of your AI efforts is directly correlated to successfully navigating the risks posed by AI.
As shown in the figure the four parts of the Collibra AI Governance framework are:
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