Posts for Tag: AI Risk Management Framework

Managing the Risks of Artificial Intelligence: A Core Idea from the NIST AI Risk Management Framework

Artificial intelligence (AI) has brought astounding advances, from self-driving cars to personalized medicine. However, it also poses novel risks. How can we manage the downsides so AI's upsides shine through? The US National Institute of Standards and Technology (NIST) offers a pioneering perspective in its AI Risk Management Framework. 

At its heart, the framework views AI risks as socio-technical - arising from the interplay of technical factors and social dynamics. If deployed crudely, an AI system designed with the best intentions could enable harmful discrimination. And even a technically sound system might degrade performance over time as society changes. Continual adjustment is critical. The framework outlines four core functions - govern, map, measure, and manage. 

"Govern" focuses on accountability, culture, and policies. It asks organizations to clearly define roles for governing AI risks, foster a culture of responsible AI development, and institute policies that embed values like fairness into workflows. Wise governance enables the rest.

"Map" then surveys the landscape of possibilities - both beneficial uses and potential downsides of a planned AI system. Mapping elucidates the real-world context where a system might operate, illuminating risks.

"Measure" suggests concrete metrics to track those risks over an AI system's lifetime, enabling ongoing vigilance. Relevant metrics span technical dimensions like security vulnerabilities to societal measures like discriminatory impacts. 

Finally, "manage" closes the loop by prioritizing risks that surfaced via mapping and measurement, guiding mitigation efforts according to tolerance levels. Management also includes communication plans for transparency.

At CPROMPT.AI, these functions tangibly guide the development of our easy-to-use platform for no-code AI. We continually map end-user needs and potential misuses, instituting governance policies that embed beneficial values upfront. We measure via feedback loops to catch emerging issues fast. We actively manage - and adjust policies based on user input to keep risks low while enabling broad access to AI's benefits.

The framework highlights that AI risks can never be "solved" once and for all. Responsible AI requires a sustained, collaborative effort across technical and social spheres - achieving trust through ongoing trustworthiness. Top Takeaways:

  • AI risks are socio-technical - arising from technology and social dynamics. Both angles need addressing.
  • Core risk management functions span governing, mapping, measuring, and managing. Each enables managing AI's downsides amid its upsides.
  • Mapping helps reveal risks and opportunities early by understanding the context thoroughly.
  • Measurement tracks technical and societal metrics to catch emerging issues over time.
  • Management closes the loop - mitigating risks based on tolerance levels and priorities.

At CPROMPT.AI, we're putting these ideas into practice - enabling anyone to build AI apps quickly while governing use responsibly. The future remains unwritten. We can shape AI for good through frameworks like NIST's guiding collective action.

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Managing AI Risks: A Framework for Organizations


Q: What is the NIST AI Risk Management Framework?

The NIST AI Risk Management Framework guides organizations in managing the potential risks of developing, deploying, and using AI systems. It outlines four core functions – govern, map, measure, and control – to help organizations build trustworthy and responsible AI. 

Q: Who can use the NIST AI Risk Management Framework? 

The framework is designed to be flexible for any organization working with AI, including companies, government agencies, non-profits, etc. It can be customized across sectors, technologies, and use cases.

Q: What are some unique AI risks the framework helps address?

The framework helps manage amplified or new risks with AI systems compared to traditional software. This includes risks related to bias, opacity, security vulnerabilities, privacy issues, and more arising from AI's statistical nature and complexity.

Q: Does the framework require specific laws or regulations to be followed?

No, the NIST AI Risk Management Framework is voluntary and complements existing laws, regulations, and organizational policies related to AI ethics, safety, etc. It provides best practices all organizations can apply.

Q: How was the NIST AI Risk Management Framework created?

NIST developed the framework based on industry, academia, civil society, and government input. It aligns with international AI standards and best practices. As a "living document," it will be updated regularly based on user feedback and the evolving AI landscape.


  • Socio-technical - relating to the interplay of social and technological factors
  • Governance - establishing policies, accountability, and culture to enable effective risk management 
  • Mapping - analyzing the landscape of possibilities, risks, and benefits for a particular AI system
  • Measurement - creating and tracking metrics that shed light on a system's technical and societal performance