What is fundamental to both AI governance principles and frameworks?

The essence of AI governance principles and frameworks lies in the need for a contextual understanding of AI application risks. This understanding is crucial because different AI systems can have vastly different implications depending on the environment in which they are deployed, the data they use, and the specific purposes they serve.

AI governance involves identifying, assessing, and mitigating risks associated with AI technologies. By grasping the context in which AI operates, stakeholders can develop tailored strategies that address specific risks, ensuring responsible use and fostering trust in AI systems. This contextual approach enables organizations to prioritize resources effectively and engage in meaningful governance practices that resonate with the unique challenges posed by their AI applications.

In contrast, the other options suggest approaches that may not align with the realities of AI governance. For instance, harmonized regulations across countries are valuable but often unrealistic due to differing legal frameworks and cultural values. Eliminating all risks is impractical as risk management focuses on mitigation rather than total eradication. Similarly, strict adherence to a singular framework can stifle innovation and adaptability, as various organizations may require different approaches suited to their specific circumstances and the unique risks they face.