Key Metrics for Measuring Success in AI Governance and Building Trustworthy Systems

What Are the Key Metrics for Measuring AI Governance?

Measuring success in AI ethics and governance isn’t about ticking boxes or drafting policies. It’s about embedding ethical AI principles deeply into your strategy, workflows, and decision-making processes. Success happens when teams feel empowered to question AI outputs, impacted communities are involved, and accountability is built into every stage of model development.

One critical aspect often overlooked is human behavior—the way people interact with AI that’s supposed to enhance their intelligence. What behaviors are you tracking? What behaviors do you want to encourage? Because in human psychology, you tend to get more of what you measure.

Finding Purpose in Governing AI

Here’s a real example: a large police department that created an AI governing council composed mostly of policing experts, not AI specialists. Initially, many council members weren’t sure why they were involved. Their strength was deep knowledge of policing, not AI or machine learning. But that expertise was exactly what was needed.

For AI to work well in this context, putting people first is essential. Domain experts understand the data, its context, and the connections between data points. They ensure AI is developed, maintained, and governed responsibly.

To engage the council, design thinking exercises were used, focusing on questions like:

• Do we have the right people in the room?
• What core problem are we trying to solve?
• Do we have the right data and domain knowledge?
• Which AI principles must be reflected to earn public trust?
• What are the unintended effects of AI models and how can we mitigate them?
• Who are the personas we’re building for?
• What communication is needed about intended and unintended AI uses?

This process requires humility, inclusivity, psychological safety, and diverse experiences. The result: teams gain a shared language to clearly express what responsible AI development and governance look like.

Measuring Human Behaviors to Ascertain Success

Since you get more of what you measure, a key question is: which human behaviors are you tracking in your AI governance efforts? Are these behaviors aligned with the messages you communicate?

Often, employees are encouraged to use AI to boost productivity, sometimes under pressure, yet held accountable for responsible outcomes without clear metrics on those outcomes. There are cases where domain experts knew AI was wrong but didn’t challenge it because their performance was measured solely on speed or output, not on quality or accountability. Some even faced consequences for contradicting AI.

To truly measure success, consider if AI actually enhances human intelligence. Are people critical consumers of AI, incentivized to help train systems or flag problems? Do they understand AI risks, bias, data issues, and accountability? Are their evaluations aligned to encourage responsible behavior?

These leadership behaviors are crucial for fostering the right organizational culture. Ultimately, success means earning trust—a challenging but essential goal for AI governance.

Becoming More AI Literate

You don’t need a PhD in AI to be part of this conversation. Your unique experiences matter. AI affects everyone—in workplaces, homes, and communities. It’s vital to ensure AI systems are safe, fair, and respect privacy.

Leading with AI requires embracing uncertainty and choosing what’s right over what’s easy or immediately profitable. It means learning how AI works, its capabilities and risks, and spreading that knowledge throughout your organization.

Investing in AI literacy and governance, and empowering capable leaders, positions you to avoid pitfalls and build trust that becomes a lasting advantage. The time to lead responsibly with AI is now.

For practical steps on advancing your AI skills and understanding governance, consider exploring comprehensive training options available at Complete AI Training.