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JOINT COMMENTS FOR AI RISK MANAGEMENT FRAMEWORK: SECOND DRAFT

BY EMPOWER AI and AMERICAN INSTITUTE OF 

ARTIFICIAL INTELLIGENCE

This is a joint submission from Empower AI and American Institute of Artificial Intelligence. We 

appreciate the opportunity to respond to the NIST risk management framework. 

Empower AI was built specifically for government missions, to solve its toughest challenges and 

elevate the full potential of the workforce. We leverage deep technical expertise and decades of 

experience solving complex challenges in Health, Defense, and Civilian missions. Our solutions 

give government leaders a direct path to meaningful transformation, equipping them with the

insights and tools necessary to make critical decisions faster and move their missions forward.

American Institute of Artificial Intelligence (hereafter American AI) generates knowledge and 

software to build the backbone of America’s artificial intelligence. 

Our Interest in AI Governance:

At Empower AI, our highest priority is to be responsible technology innovators, for our 

customers, employees, and the nation. Before a single line of code was developed for our 

Empower AI Platform®, we committed to achieve or exceed the highest standards in 

governance and ethics (G&E), including industry standards, regulations, and all frameworks and 

models developed by the U.S. government. 

American AI developed one of the most comprehensive AI ethics and governance model in 

2016 which was shared with the original team of the OECD that designed the first AI ethics and 

governance model. American AI has authored several books on artificial intelligence strategy 

and its application and importance in fields ranging from policy and government to finance and 

supply chains.

Our Comments: 

Our comments add to the goal of the NIST risk management framework: to address risks in the 

design, development, use, and evaluation of AI products, services, and systems.

We have identified nine opportunities to further enhance the framework. Some of those gaps 

are conceptual, while others are technical. 

1. Redefine the Concept of Lifecycle to Stage Transition: The concept of lifecycle as used 

in AI (and the framework) is a remnant of legacy static technology that was developed, 

used, and then retired. When it comes to AI, systems are not retired, they transform. For 

example, Google (the search engine) is essentially an AI system, but at this stage, it is 

inconceivable that the Google search engine would one day be retired – this being 

consistent with the learning capabilities of the AI systems, where the system will 
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continuously transition to the next stage of intelligence. AI systems, therefore, should be 

viewed as stage transitions and not have the traditional born-use-retire pattern. We propose 

that the concept of lifecycle in the NIST methodology should be revised and even replaced 

by a concept of stage transition. The concept of stage transition is derived from changes in 

one or more of the following: 1) Problem domain: the nature of the problem domain 

changes; 2) Features: problem representation is improved by adding or removing new 

features (variables) that were not considered before; 3) Data: Adding new data to existing 

features to improve the problem representation; and 4) Model: Better or more efficient 

models can be produced. Hence, an AI system’s lifecycle is composed of managing 

changes across these four areas of stage transition and not necessarily retiring the system.

2. Incorporate System of AI Products vs. Single Product Focus: The current NIST 

framework focuses on a single product and assumes that a single artifact is at the center of 

AI development. In industrial applications, AI products and services function as a series of 

interconnected AI and non-AI applications. These interdependencies give rise to incremental 

risks. Therefore, we believe it is essential to approach risk in terms of individual risk (as 

NIST has currently captured), process risk (risk in the process of which a certain artifact 

functions), and systemic risk (risk related to the entire broad systems). For example, an 

automated trading algorithm has risks that are part of its performance. However, the 

algorithm may work in concert with other algorithms and may increase or reduce risk related 

to the entire process (process risk). Finally, the algorithm contributes to the systemwide risk 

for the whole market (systemic). 

3. Add Legal to the Characteristics of Trustworthy Systems: The NIST framework 

identifies the key characteristics of trustworthy systems as valid and reliable, safe, fair and 

bias is managed, secure and resilient, accountable and transparent, explainable and 

interpretable, and privacy-enhanced. None of the attributes listed include legal. A system 

can meet all the above requirements and yet be illegal to build or use. For example, an AI 

system that meets all the above conditions may not meet the legal requirement of a 

particular country or region. Because laws vary from location to location, for a system to be 

reliably trustworthy, it must meet the legal definition. We recommend that NIST incorporates

language that says, “a trustworthy system must abide by the local, national, and

international laws in which it operates.” 

4. Make Model Excellence a Required Standard: Two of the most critical aspects of 

developing AI are: 1) proper representation of the problem in the model i.e., the model 

captures the problem domain in its entirety such that it solves the problem exhaustively; and 

2) the model used to solve the problem is the best and most efficient in terms of both 

training efficiency and application effectiveness. These two considerations are important 

because they create customer orientation and protect customer interests in the AI field. In 

other words, some companies that claim to develop AI solutions may not offer the best 

model-problem consistency or may not apply the best techniques that lead to the fastest 
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process for the best algorithm selection, training efficiency, and performance. We 

recommend that NIST makes model excellence (i.e. the developer will strive to select the 

most efficient model that will properly represent the stated problem, will be most efficient to 

train, and perform best) as part of the framework. This would also imply that developers of 

AI should disclose to the clients, both government agencies and the private sector, why they 

chose a particular model, the comparative statistics on the use and performance of other 

models, and training efficiency.

5. Include Evolution and Dynamics of Systems: Changes in problem domains and their 

representation are essential to AI systems. For example, a trading algorithm developed on 

trading data from the last 30 years may not capture the unique dynamics of current-day high 

inflation and rising interest rates dynamics because we have not seen such dynamics in the 

last three decades. For this reason, we recommend NIST require developers to disclose 

how the selected model will adapt to future states and trajectories of the problem domains, 

implying that as a best practice, developers will assess the future changes in the problem 

domain and provide an evaluation of how their recommended solution will adapt to those 

changes. 

6. Give Feedback to Social Systems: The NIST standard properly identifies the three forms 

of biases: systemic, computational, and human biases. The current approach, however,

raises an important issue. Since the data representing social biases represent existing 

social truths, a system designed to evade those biases will no longer reflect true social

biases. Since there will be a split between the real social constructs and the system’s 

constructs, the system will represent a reality that is untrue at a broader level. This reality 

can lead to a false impression that social evils are cured when, those biases may very well 

continue to be part of the social fabric. This reality can lead to a false impression that social 

evils are cured when those biases may very well continue to be part of the social fabric. We 

recommend that in such cases where data is altered to reflect “what should be” vs. “what is”, 

the developers of the system issue two required annual reports for public consumption: 1) 

An ongoing report that shows how the society would have made that (biased) decision vs. 

how the system made the (unbiased) decision; 2) what specific social biases the system 

overcame due to its design excellence. That way, the system will become a force for good 

where it will not only do the right thing but also create social awareness about the right 

things. The alternative is dangerous because while it solves the ethical issue of a given 

enterprise, it allows society to fester with biases. We have proposed a way for AI to become 

a source of positive change in society. 

7. AI Ethics frameworks are Mission Centric: Since both Empower AI and American 

Institute of AI deal with AI that is applied in both civilian and defense agencies, it is our 

recommendation that relativeness of mission should be considered as an important 

determinant of AI ethics for NIST. This implies that no single ethical framework can fulfill the 

mission requirements of all agencies. For example, the AI ethics framework for FBI can be 

different than for CIA or NIH or DoD. Since missions are unique to the agencies, the mission 
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should drive the appropriate ethical framework for the specific agency. We suggest NIST 

should incorporate language that clarifies the plurality of AI ethical frameworks based upon 

agencies’ missions. 

8. Include Other Lifeforms: Since AI, both in defense and civilian operations, is often 

deployed to work with nonhuman animals (for example, home cleaning robots with pets, law 

enforcement dogs, farm animals, etc.), we suggest that the language of Section 4.2 (Safe, 

characteristic of trustworthy systems) should add, “nonhuman animals.” After the proposed 

amendment, the Safe standard will read: AI systems “should not, under defined conditions, 

cause physical or psychological harm or lead to a state in which human life, health, property, 

nonhuman animals, or the environment is endangered”. While animals are included under 

human property, due to the nature of the AI systems, we suggest that nonhuman animals, 

especially service animals, should be included as a separate reference consistent with 

legislation, and court decisions, and the stated vision of Animal Legal Defense Fund.

9. Add Reference to Geopolitical Concerns: The rise of geopolitical conflict, more capable 

adversaries, and great power competitors require us to reconsider our existing frameworks. 

We strongly suggest that NIST incorporate language requiring developers to consider the 

risks associated with involving and dealing with adversaries, their companies, and supply 

chains. Both data and application value chains should be assessed for risks related to data, 

technology, methodology, software, intellectual property, or models coming from or ending 

up in the hands of geopolitical adversaries. While such a requirement will not be necessary

during normal times of a globalized economy, we believe that the US policy of limiting the 

access and involvement of potent adversaries (particularly in AI) in American business and 

the technological race between geopolitical rivals, the concern rises to a level where it 

should be formally reflected and adopted in the NIST standard. 

We appreciate the opportunity to comment. 

Sincerely, 

Paul Dillahay Dr. Al Naqvi

President and CEO Founder and Chief Executive Officer

Empower AI American Institute of Artificial Intelligence 

Al Naqvi 

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