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Your governance needs to learn and adapt, or it becomes irrelevant quickly. We build learning systems around the opacity rather than trying to eliminate it. working through these ideas in more detail—how cybernetic principles apply to AI governance, what this means

This means being careful about where your data comes from, protecting privacy, and being open about how professionals are moving into AI governance roles, but fewer are pursuing AI engineering due to the longer lead challenge is more pervasive than previous BYOD issues, as AI tools are easily accessible online and often leave This highlights how AI governance is emerging from IT but needs to learn from industries with long histories

One of the powers of technology is its ability to externalize the means to achieve our ends. It is externalizing the means by which we learn to the point that we don’t need to learn ourselves. What if meaning is found not by having the goal of our desire but instead by our participation in the means to make it happen.

This approach is also not without its problems as it can sometimes lead to "blaming" the system and loss Root Cause Analysis Dean Gano, the creator of the Apollo Root Cause Analysis method [2][3], goes back This leads to the conclusion that a cause and an effect are the same thing.

Operational Rings of Power These are held together by the fellowship of: 🔸 Feed Forward Processes - leading

Are you someone who believes that taking risks always leads to negative outcomes? In the world of risk management, experts often argue that risk is always bad, negative, and leads to What if we told you that that risk can lead to positive outcomes and success? They knew that taking a chance means embracing uncertainty and the possibility of both good and bad outcomes

together, improve the probability of success by governing, regulating, and ensuring the ends and the means If you're interested in learning more about transforming reactive GRC functions into proactive GRE capabilities

We learn through observation and adaptation rather than pre-planning. 4. plan for every contingency, successful AI engineering embraces iterative development with continuous learning Because these systems continuously learn, adapt, and evolve, yesterday's test results may not predict In practical terms, this means developing systems where: We can reasonably predict the boundaries of aspects of system performance We can make evidence-based improvements based on real-world operation Learning

that systems become more complex over time which results in the emergence of new uncertainty which leads This is one of the reasons why you need to be proactive which means: anticipate, plan and act to prevent

This may lead to non-compliance with safety and security regulations along with breaking promises made This, in turn, attracts and retains talent, enhances brand reputation, and ultimately leads to corporate This leads to a Total Value Advantage , fostering trust, building resilience, and ultimately achieving

Smart Predictive KPIs : anticipate future performance, producing reliable leading indicators and providing However, leading indicators is a different story. Leading indicator are the holy grail of operational performance and require knowledge of what should It's helpful to remember that there are other forces at work: You can’t turn lagging indicators into leading

operational or enterprise systems introduces unacceptable risks, as even minor algorithmic errors can lead