(Cross-post from the EA Forum)
The US National Institute of Standards and Technology (NIST) previously put out a request for information (RFI) to help shape the AI Risk Management Framework that it is working on. NIST is an important organization in establishing standards for government and industry, so contributions here could potentially be high-impact.
The deadline for feedback has recently been extended to September 15, so if you want to contribute then now is the time to do so.
In particular, they are looking for information on:
1. The greatest challenges in improving how AI actors manage AI-related risks—where “manage” means identify, assess, prioritize, respond to, or communicate those risks;
2. How organizations currently define and manage characteristics of AI trustworthiness and whether there are important characteristics which should be considered in the Framework besides: Accuracy, explainability and interpretability, reliability, privacy, robustness, safety, security (resilience), and mitigation of harmful bias, or harmful outcomes from misuse of the AI;
3. How organizations currently define and manage principles of AI trustworthiness and whether there are important principles which should be considered in the Framework besides: Transparency, fairness, and accountability;
4. The extent to which AI risks are incorporated into different organizations' overarching enterprise risk management—including, but not limited to, the management of risks related to cybersecurity, privacy, and safety;
5. Standards, frameworks, models, methodologies, tools, guidelines and best practices, and principles to identify, assess, prioritize, mitigate, or communicate AI risk and whether any currently meet the minimum attributes described above;
6. How current regulatory or regulatory reporting requirements (e.g., local, state, national, international) relate to the use of AI standards, frameworks, models, methodologies, tools, guidelines and best practices, and principles;
7. AI risk management standards, frameworks, models, methodologies, tools, guidelines and best practices, principles, and practices which NIST should consider to ensure that the AI RMF aligns with and supports other efforts;
8. How organizations take into account benefits and issues related to inclusiveness in AI design, development, use and evaluation—and how AI design and development may be carried out in a way that reduces or manages the risk of potential negative impact on individuals, groups, and society.Start Printed Page 40813
9. The appropriateness of the attributes NIST has developed for the AI Risk Management Framework. (See above, “AI RMF Development and Attributes”);
10. Effective ways to structure the Framework to achieve the desired goals, including, but not limited to, integrating AI risk management processes with organizational processes for developing products and services for better outcomes in terms of trustworthiness and management of AI risks. Respondents are asked to identify any current models which would be effective. These could include—but are not limited to—the NIST Cybersecurity Framework or Privacy Framework, which focus on outcomes, functions, categories and subcategories and also offer options for developing profiles reflecting current and desired approaches as well as tiers to describe degree of framework implementation; and
11. How the Framework could be developed to advance the recruitment, hiring, development, and retention of a knowledgeable and skilled workforce necessary to perform AI-related functions within organizations.
12. The extent to which the Framework should include governance issues, including but not limited to make up of design and development teams, monitoring and evaluation, and grievance and redress.
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