Foundational principles translate into actionable risk management frameworks by establishing the high-level ethical and societal “what” of AI governance, which organizations then address through practical, lifecycle-focused tools designed for deployment.
The sources highlight a clear hierarchy in AI strategy development, moving from foundational context to direct, actionable frameworks.
1. Foundational principles (defining the “what”)
Foundational principles establish the necessary ethical and high-level considerations that must guide AI development and deployment globally.
Example: OECD AI Principles and Recommendations for Policymakers. This resource serves as a foundational intergovernmental standard.
It outlines the core ethical standards that both companies and policymakers must consider.
Specifically, these principles focus on values such as human rights and democratic values.
This report provides the essential “what” of AI governance, setting the broad, normative boundaries for responsible technology use.
2. Actionable frameworks (defining the “how”)
Actionable risk management frameworks take these foundational principles and translate them into concrete organizational processes for practical deployment.
Example: AI Risk Management Framework (AI RMF) by NIST. This document is considered the most important for practical deployment.
It functions as a voluntary, actionable framework specifically designed for organizations.
The framework enables organizations to operationalize the foundational principles by performing key risk management activities:
Identify risks associated with AI systems.
Assess these risks.
Manage these risks throughout the AI system’s lifecycle.
The translation occurs when an organization uses an actionable framework (like the NIST AI RMF) to systematically address the requirements laid out by foundational principles (like the OECD principles). The principles define whatethical standards must be met (e.g., ensuring AI respects human rights), while the framework defines how an organization structures its processes to achieve that objective (e.g., identifying, assessing, and mitigating risks related to bias or misuse throughout the AI system’s lifecycle).
Ultimately, the actionable framework moves the discussion from global policy and academic context into the technical and organizational steps necessary for responsible development and management.
🚨❓Poll: Translating principles into actionable adoption
For organizations seeking to move beyond high-level principles to practical deployment, which document provides the voluntary, actionable framework necessary to operationalize principles by performing systematic risk identification, assessment, and management throughout the AI system’s lifecycle?
A. Governing for Good and for All (LCFI)
B. AI governance: a systematic literature review (ResearchGate)
C. AI Risk Management Framework (AI RMF) (NIST)
D. AI Principles and Recommendations for Policymakers (OECD)
🚨❓Poll: How do foundational principles translate into actionable risk management frameworks for organizations?
Foundational principles translate into actionable risk management frameworks by establishing the high-level ethical and societal “what” of AI governance, which organizations then address through practical, lifecycle-focused tools designed for deployment.
The sources highlight a clear hierarchy in AI strategy development, moving from foundational context to direct, actionable frameworks.
1. Foundational principles (defining the “what”)
Foundational principles establish the necessary ethical and high-level considerations that must guide AI development and deployment globally.
Example: OECD AI Principles and Recommendations for Policymakers. This resource serves as a foundational intergovernmental standard.
It outlines the core ethical standards that both companies and policymakers must consider.
Specifically, these principles focus on values such as human rights and democratic values.
This report provides the essential “what” of AI governance, setting the broad, normative boundaries for responsible technology use.
2. Actionable frameworks (defining the “how”)
Actionable risk management frameworks take these foundational principles and translate them into concrete organizational processes for practical deployment.
Example: AI Risk Management Framework (AI RMF) by NIST. This document is considered the most important for practical deployment.
It functions as a voluntary, actionable framework specifically designed for organizations.
The framework enables organizations to operationalize the foundational principles by performing key risk management activities:
Identify risks associated with AI systems.
Assess these risks.
Manage these risks throughout the AI system’s lifecycle.
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The translation occurs when an organization uses an actionable framework (like the NIST AI RMF) to systematically address the requirements laid out by foundational principles (like the OECD principles). The principles define whatethical standards must be met (e.g., ensuring AI respects human rights), while the framework defines how an organization structures its processes to achieve that objective (e.g., identifying, assessing, and mitigating risks related to bias or misuse throughout the AI system’s lifecycle).
Ultimately, the actionable framework moves the discussion from global policy and academic context into the technical and organizational steps necessary for responsible development and management.
🚨❓Poll: Translating principles into actionable adoption
For organizations seeking to move beyond high-level principles to practical deployment, which document provides the voluntary, actionable framework necessary to operationalize principles by performing systematic risk identification, assessment, and management throughout the AI system’s lifecycle?
A. Governing for Good and for All (LCFI)
B. AI governance: a systematic literature review (ResearchGate)
C. AI Risk Management Framework (AI RMF) (NIST)
D. AI Principles and Recommendations for Policymakers (OECD)
Looking forward to your answers and comments,Yael Rozencwajg
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