Governance roles and responsibilities
1. Governance roles and responsibilities
Welcome back. In this next part, we shift focus from the what and how of AI governance to the who. We'll explore key roles in effective AI governance and how responsibilities span technical and non-technical teams. Most importantly, we'll see why cross-functional collaboration is vital for successful governance execution. Iason: Simla, our last conversation clarified the components of AI governance. Now, I'm curious—who's actually responsible for making AI governance work within an organization? Simla: That's a great question, Iason. AI governance is indeed a team effort, involving a variety of roles across different departments. Influential bodies like the International Association of Privacy Professionals (IAPP) and global standards developers such as the International Organization for Standardization (ISO) – specifically with their AI Management System standard ISO 42001- encourage organizations to establish clear, structured roles to manage AI responsibly. Iason: Can you give me an overview of these roles? Simla: Absolutely. While job titles vary, there are a few key functions involved in AI governance. (Another slide) First, you have strategic leaders who set direction and make sure AI aligns with company values and standards like ISO 42001. Then there are risk, compliance, and legal teams who focus on regulations and ethical use. Data governance folks ensure the data feeding AI is trusted, documented, and traceable—so things like lineage and stewardship really matter here. InfoSec teams play a growing role too, keeping models and data safe from attacks or misuse. And of course, technical teams—data scientists and engineers—who build and test models within those governance boundaries. Finally, business teams help make sure AI delivers value and is applied responsibly in real-world contexts. The key is cross-functional collaboration—AI governance isn’t owned by one group, it’s shared across the organization. Iason: Interesting. So, it's not just the technical team involved in AI governance? Simla: Exactly. While technical roles like data scientists and engineers are crucial, non-technical stakeholders play significant roles too. Iason: How do these diverse roles collaborate effectively? Simla: Cross-functional collaboration is essential. Organizations often establish AI governance committees or working groups that include representatives from various departments. These groups work together to develop policies, assess risks, and oversee AI deployments. For instance, imagine a company is developing a new AI-powered chatbot for customer service. The governance committee would bring together a privacy officer to ensure data handling aligns with GDPR or PIPEDA, a user experience (UX) designer to ensure the chatbot is transparent about its AI nature and easy for customers to understand, an IT security expert to assess potential vulnerabilities, and a legal counsel to review disclaimers and terms of service. They collectively ensure the chatbot is compliant, ethical, and user-friendly before it ever interacts with a customer. Iason: That makes sense. So, does effective AI governance require both technical expertise and organizational oversight? Simla: Precisely. By involving a diverse set of stakeholders, organizations can better manage AI's ethical, legal, and operational aspects. This holistic approach is central to frameworks like ISO/IEC 42001, which advocates for integrating AI governance into existing organizational structures. Iason: Thanks, Simla. This gives me a clearer picture of how AI governance functions within an organization. Simla: You're welcome, Iason. Remember, successful AI governance is a collaborative effort that spans the entire organization.2. Let's practice!
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