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Following the laws of cybernetics, to be a good regulator they must be a model of the system they are In this field, "freezing" a model is a critical strategy to ensure consistent performance and safety. Our governance models must innovate beyond traditional, static approaches and embrace the inherent complexity

companies are viewing customers as a means to an end – fuel for their AI engines, shiny generative models (to name a few) have already scraped much (if not all) of the internet to train their generative AI models

Transparency in AI algorithms, data privacy protection, and addressing bias in machine learning models XAI emphasizes transparency and interpretability in AI systems, ensuring that decisions made by AI models

organizations the essential concepts and principles that underlie management programs based on an operational model This model incorporates the science of governing, systems, risk, and promise theories, along with Lean

recent years, standards and regulations have specified the need for companies to adopt improvement models The need for maturing systems is not new and there are many: methods, frameworks, and models that can

These programs are based on reactive models characterized by: lagging indicators, audit-fix cycles, and Unfortunately, GRC appears to be preoccupied with this same reactive model correcting things after they The reactive model on which GRC is based is not enough to achieve the desired outcomes for GRC never A proactive model would function as a regulated system (in the technical sense) focused on outcomes,

The PLUS Decision Making model developed by The Ethics Resource Center can help organization’s make generations to meet their own needs.” - United Nations This is another test we could add to the PLUS model

AI algorithms within digital twins enable predictive modeling and optimization, but they may also amplify Additionally, complex AI models may lack interpretability, hindering decision-makers' ability to trust

This requires a more thorough discussion on the nature of uncertainty, cause/effect models, and working tools and practices (probabilities, likelihoods, Monte Carlo, estimation techniques, aggregation, modelling

To understand this better we developed the following compliance operational model: This model comprises MVC is not achieved at the end of a 5-step maturity model, but right at the start.

I knew that information about Lean Compliance used in current foundation models was not up-to-date and

In this presentation he proposed the digital twin as a conceptual model underlying a product life-cycle However, the idea of modelling the real-world with computer simulation is not new and can go back to What makes digital twins different from computer-based modelling are the connections between the real In essence, a model becomes a digital twin when it connected with its real life counterpart. The digital thread is a critical capability in model-based systems engineering (MBSE) and the foundation