What is The NIST Artificial Intelligence Framework

Part I also describes trustworthy AI systems, including characteristics such as validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy-enhanced, and fairness with harmful bias managed.

Part II describes features to address risks associated with the use and deployment of AI systems. These features include:

1. Governance: a culture of risk management;
2. Mapping: context is recognized and risks identified;
3. Measurement: identified risks are assessed, analysed or tracked; and
4. Management: risks are prioritized and acted upon based on a projected impact.

The AI RMF can be applied wherever AI may form part of a business. As it is considered for voluntary use it is not required, but importantly it demonstrates that a business is considering the risk associated to AI and working on a governance process to mitigate these risks. The reputational impact that this can have could be very positive for an organisation as it demonstrates a responsible approach to AI, but also importantly provides a resilient and robust framework for managing an AI over its lifecycle.

This fundamentally promotes trust, but the framework also encourages diversity of stakeholders, to maximise diversity of thought, and knowledge of risks. This is an important aspect as it allows for any system built on this to be considered for all points in its lifecycle from Development through to Deployment and maintenance.

Likely quick adopters of this framework will be:

1. Government;
2. Defence;
3. Finance;
4. Policing and Security;
5. Healthcare;
6. Safety Critical Applications.

The Framework is designed to equip organizations and individuals – referred to as AI actors – with approaches that increase the trustworthiness of AI systems, and to help foster the responsible design, development, deployment, and use of AI systems.

The main reason the AI RMF is important is that it is a methodical framework for managing AI risk, which incorporates global standards for ISO, IEEE, OECD. This will likely form the foundation for future standard which may become a route for certification of de-risked AI systems.

Additionally governments and regulators are looking to design regulation for AI that will be used to protect society from adverse activities, and manage the risks of AI at a policy level.

Future regulation will strongly borrow from this Risk Framework, and will also align with emerging standards as a whole.

Advai is a deep tech AI start-up based in the UK that has spent several years working with UK government and defence to understand and develop tooling for testing and validating AI in a manner that allows for KPIs to be derived throughout its lifecycle that allows data scientists, engineers, and decision makers to be able to quantify risks and deploy AI in a safe, responsible, and trustworthy manner.

If you would like to discuss this in more detail, please reach out to contact@advai.co.uk

Here are a few links that you might find useful if you are interested in learning more about Robust AI:

1. Artificial Intelligence Risk Management Framework (AI RMF 1.0) (nist.gov)
2. https://pages.nist.gov/AIRMF/
3. US Government Releases Artificial Intelligence Governance Framework (friedfrank.com)
4. How to Prepare for (or Implement) the AI Bill of Rights - RTInsights