Responsible Al Frameworks: An Overview

Introduction

The rapid growth and widespread adoption of Artificial Intelligence (AI) have led to a pressing need for frameworks that can help policymakers, regulators, and other stakeholders assess and manage the risks and opportunities associated with AI systems. 

In this blog, we will explore three key frameworks that are shaping the AI landscape: the NIST AI Risk Management Framework, the OECD Framework for the Classification of AI Systems, and the Model Governance Framework by BSA.

NIST AI Risk Management Framework

The National Institute of Standards and Technology (NIST) issued Version 1.0 of its Artificial Intelligence Risk Management Framework (AI RMF) in January 2023. This framework is designed to guide the development of trustworthy and responsible AI systems by providing a flexible approach to AI risk management. The AI RMF is rooted in NIST’s culture of precise measurement and is intended to be replicated for practical application.

OECD Framework for the Classification of AI Systems

The OECD Framework for the Classification of AI Systems is a user-friendly tool developed by the Organisation for Economic Co-operation and Development (OECD) to evaluate AI systems from a policy perspective. The framework consists of five categories that examine AI systems based on how they affect people and the planet, the economic context, the data used, the AI model, and the tasks performed. This framework helps policymakers identify specific risks associated with AI, such as bias, explainability, and robustness.

Model Governance Framework by BSA

The Model Governance Framework by BSA (Business Software Alliance) is a proposed framework for governing generative AI. The framework aims to provide guardrails for the development and deployment of generative AI systems, ensuring that they are trustworthy and responsible. The framework is designed to be flexible and adaptable to different contexts and applications.

Key Takeaways

1. Risk Management: The NIST AI RMF emphasizes the importance of risk management in AI development, highlighting the need for organizations to design and manage trustworthy and responsible AI systems.
2. Classification: The OECD Framework for the Classification of AI Systems provides a structured approach to classifying AI systems based on their impact on people, the planet, and the economy.
3. Governance: The Model Governance Framework by BSA emphasizes the need for governance structures to ensure the responsible development and deployment of generative AI systems.

Conclusion

These three frameworks demonstrate the growing recognition of the need for structured approaches to AI development, deployment, and governance. As AI continues to transform industries and societies, it is essential that policymakers, regulators, and other stakeholders have access to robust frameworks that can help them navigate the complexities and risks associated with AI. By understanding these frameworks, we can work towards creating a safer and more responsible AI ecosystem.

References

1. OECD Framework for Classifying AI Systems: https://survey.oecd.org/index.php?lang=en&r=survey%2Findex&sid=178985
2. OECD Framework for Classification of AI Systems: https://indiaai.gov.in/news/oecd-framework-for-classification-of-ai-systems-to-augment-national-ai-strategies
3. NIST AI Risk Management Framework: https://www.brookings.edu/articles/nists-ai-risk-management-framework-plants-a-flag-in-the-ai-debate/
4. Model Governance Framework by BSA: https://www.bsa.org/policy-filings/singapore-bsa-comments-on-public-consultation-on-proposed-model-ai-governance-framework-for-generative-ai