Unpacking AI Governance Principles and Frameworks

Working towards understanding AI Governance, I’ve noticed some confusion between two key concepts: principles and frameworks. Principles are foundational guidelines that shape our approach. They’re broad, overarching ideas that inform decision-making, often rooted in philosophical “isms” like utilitarianism, deontology, or virtue ethics. These high-level ethical principles are then translated into more specific AI principles such as transparency, fairness, and accountability.

Examples of principle sets include the OECD AI Principles, UNESCO’s Recommendation on the Ethics of AI, the Rome Call for AI Ethics, and the Asilomar AI Principles.

Frameworks, on the other hand, are structured approaches to implement these principles. They provide detailed, actionable plans with specific steps. Some examples are the NIST AI Risk Management Framework, the EU AI Act, Singapore’s AI Governance Framework with its updated framework to also cover Generative AI, and IEEE’s Ethically Aligned Design.

Think of principles as the ‘why’ and frameworks as the ‘how’. Principles provide the ethical compass, while frameworks offer the roadmap to turn those ideals into reality. Understanding this difference is crucial for effective AI governance. It helps us build comprehensive strategies that are both ethically grounded and practically applicable.