Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19 

All comments will be made public as-is, with no edits or redactions. Please be careful to not include confidential business or personal information, otherwise sensitive or protected information, or any information you do not wish to be posted. 

Comment Template for Submit comments by August 19, 2021: 

Responses to NIST Artifical 

Intelligence Risk 

Management Framework 

General RFI Topics (Use as many 

lines as you like) 

Response # Responding 

organization 

Responder's 

name 

Paper 

Section (if 

applicable) 

Response/Comment (Include rationale) Suggested change 

Keep focusing on and delineate the 

meaning of societal-scale issues, to 

include: risks to democracy and 

security; risks to human rights and 

wellbeing; and global catastrophic 

risks. 

1 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

We appreciate that NIST has dedicated substantial attention to societal-scale issues in the AI RMF RFI, in addition to 

individual and group risks. We recommend that the focus of impacts on society remain and for the meaning of 

societal-scale issues to be expanded to include: 

1.Risks to democracy and security such as polarization, extremism, disinformation, and social manipulation; 

2.Risks to human rights and wellbeing including equity, environmental, and public health risks; and 

3.Global catastrophic risks including risks to large numbers of people caused by AI accidents, misuse, or unintended 

impacts in both the near- and long-term. 

These categories are not mutually exclusive, and other categories also could be worth including. 

Risks to Democracy and Security 

Societal risks include that personalized disinformation (enabled by AI) on social media (e.g., through Twitter bots, 

synthesis of massive datasets from Facebook, deepfake videos) can sway elections (Brkan 2019) and incite genocide 

(Mozur 2018). AI-enabled automated surveillance systems could suppress dissent, and hackers can use AI to 

augment their capability for cyberattacks, including on critical infrastructure (Brundage et al. 2018). 

Risks to Human Rights and Wellbeing 

In addition to risks to democracy from AI-enabled disinformation, we have also seen throughout the COVID-19 

pandemic the role of mis- and disinformation on public health outcomes, which is a major component of human 

rights and wellbeing. 

The 2021 National Defense Authorization Act (NDAA) authorizes the Secretary of Commerce to establish a National 

AI Ad i C i i l di S b i AI d L E f id id i h 

We recommend that the meaning of societal scale issues be expanded to include: risks to 

democracy and security such as polarization, extremism, mis- and disinformation, and 

social manipulation; risks to human rights and wellbeing including equity, environmental, 

and public health risks; and global catastrophic risks, including risks to large numbers of 

people caused by AI accidents, misuse, or unintended impacts in both the near- and longterm. 

Risk assessment approaches focused 

on intended use cases have 

important limitations. 

2 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

Consideration of intended AI use-cases is valuable and necessary, but not sufficient, for identification and assessment 

of important AI risks. We appreciate that NIST goes beyond focusing on intended use cases in the AI RMF RFI section 

Supplementary Information, Genesis for Development of the AI Risk Management Framework. That section states 

that “With broad and complex uses of AI, the Framework should consider risks from unintentional, unanticipated, or 

harmful outcomes that arise from intended uses, secondary uses, and misuses of the AI” and that the RMF should 

“be adaptable to many different organizations, AI technologies, lifecycle phases, sectors, and uses.” However, NIST 

does not clearly indicate scope beyond intended use cases when the NIST AI RMF RFI section Supplementary 

Information, AI RMF Development and Attributes, attribute 5, states that “...The Framework should assist those 

designing, developing, using, and evaluating AI to better manage AI risks for their intended use cases or scenarios.” 

A focus on intended use cases could miss other foreseeable use cases and misuses. The limitations of a use case 

focused approach become more important as new AI systems become increasingly general in capability, with 

greater potential for adaptation to new uses (and misuses) across application domains. As an example of new AI 

systems with increasing generality of applicability, GPT-3 generated text with performance comparable to, or in 

some cases better than, task-specific fine-tuned systems (Brown et al. 2020). For discussion of the importance of 

considering potential misuse of AI, see, e.g., Brundage et al. (2018). The EU AI Act also includes the general idea of 

considering “reasonably foreseeable misuse” along with an “intended purpose” of an AI system (EU 2021). 

We recommend that the RMF include clear, usable guidance on identifying and assessing risks of potential uses, 

yielding risk management strategies that would be robust in the face of high uncertainty about future potential uses 

and misuses beyond the AI designers’ originally intended/planned uses. For example, to anticipate potential misuses, 

NIST h ld id h b i f id if i d i i k f " i " “ b ” 

We recommend that the RMF include clear, usable guidance on identifying and assessing 

risks of AI, yielding risk management strategies that would be robust despite high 

uncertainty about future potential uses and misuses beyond the AI designers’ originally 

intended/planned uses. 

Type: E - Editorial, G - General T - Technical 1 of 6 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19 

The nascent but growing field of AI 

safety is providing insights about AI 

risks and risk management. 

3 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

While much of the work in the field of AI safety is at an early stage, it has already yielded some general principles 

and tools that we expect could be useful to NIST stakeholders. For examples of resources that include concepts or 

tools for technical specialists in testing key aspects of AI safety, see Amodei et al. (2016), Ray et al. (2019), and 

OpenAI (2019a, 2019b). 

Work adjacent to the field of AI safety has also highlighted the distinctive risks of formal models and real-world 

systems. This includes distinguishing the optimization of some represented task as part of a model vs. establishing 

control and stability over the dynamics of the domain in interaction with a given AI system. For a sociotechnical 

presentation that highlights important dimensions of this problem, see Andrus et al. (2020) and Dean et al. (2021). 

The lack of clear or agreed-upon definitions for terms like "trustworthiness" and "safety" is now being examined by 

safety researchers (Dobbe et al. 2021). In addition, the Georgetown University Center for Security and Emerging 

Technology (CSET) briefs on AI safety provide summaries for broad audiences; see Rudner and Toner (2021a, 2021b, 

2021c). 

Our points on this cross-cutting topic relate to several specific topics in the RFI, including: challenges in risk 

management (Topic 1), definitions of AI characteristics such as safety (Topic 2), AI risk management principles (Topic 

7), and risk to society (Topic 8). 

We recommend that the NIST Framework consider the nascent but growing field of AI safety in informing its 

deliberations. 

We recommend that the NIST Framework consider the nascent but growing field of AI 

safety in informing its deliberations. 

NIST should continue to maintain 

awareness of progress in AI safety 

and other key fields, and update 

corresponding components of the 

RMF as needed. 

4 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

The AI field has changed significantly over the last five years, and is likely to continue to change, perhaps even more 

dramatically. Ongoing research, particularly in such critical domains as AI safety, security, and capabilities will 

demand that the Framework is flexible enough to withstand potential shifts, and that NIST update corresponding 

components of the Framework as needed. To follow shifts across these fields and potential impact on the RMF, we 

recommend that NIST maintain close relationships with researchers in key fields, such as AI safety, security, and 

capabilities. These include researchers at three UC Berkeley research centers: the Center for Human-Compatible AI 

(CHAI), the Center for Long-Term Cybersecurity (CLTC), and the Center for Information Technology Research in the 

Interest of Society (CITRIS). 

Our points on this cross-cutting topic relate to several specific topics in the RFI, including: challenges in risk 

management (Topic 1), definitions of AI characteristics such as safety (Topic 2), AI risk management methodologies 

(Topic 5), and risk to society (Topic 8). 

We recommend that NIST maintain close relationships with researchers in key fields (including AI safety, security and 

capabilities) to follow shifts across these fields and potential impact on the RMF, and that NIST update 

corresponding components of the Framework as needed. 

We recommend that NIST maintain close relationships with researchers in key fields 

(including AI safety, security and capabilities) to follow shifts across these fields and 

potential impact on the RMF, and that NIST update corresponding components of the 

Framework as needed. 

Coordination of standards for risk 

identification and mitigation, to the 

extent possible. 

5 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

The NDAA requests that NIST ensure the Framework “align(s) with international standards, as appropriate.” 

Development and deployment of AI systems is often global. To better support efficiency and effectiveness in 

implementation of standards to identify and mitigate risks of AI, NIST should coordinate development of any AI 

standards with standards development organizations, including the Institute of Electrical and Electronics Engineers 

(IEEE), the International Standards Organization (ISO), the International Electrotechnical Commission (IEC), the 

European Committee for Standardization (CEN) and the European Committee for Electrotechnical Standardization 

(CENELEC), among others. 

While standards may provide guidance on appropriate criteria to evaluate AI, it is important that standards are 

carefully developed to ensure relevant criteria are considered. If criteria in the Framework and corresponding 

standards are too narrow, they may inadvertently overlook potential risks. NIST’s commitment to a flexible 

Framework that is consistently updated is critical to ensure appropriate identification and mitigation of risks. 

Our points on this cross-cutting topic relate to AI RMF attribute #7, as well as RFI topics #1 and #5. 

We recommend that NIST be explicit about how and where the RMF will incorporate and coordinate with existing 

and future AI standards development and risk assessment. 

We recommend that NIST be explicit about how and where the RMF will incorporate and 

coordinate with existing and future AI standards development and risk assessment. 

Responses to Specific Request for 

information (pages 11,12, 13 and 14 

of the RFI) 

Type: E - Editorial, G - General T - Technical 2 of 6 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19 

1. The greatest challenges in 

improving how AI actors manage AIrelated risks – where “manage” 

means identify, assess, prioritize, 

respond to, or communicate those 

risks; 

6 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

A general challenge is the identification, assessment and prioritization of risks that could have high consequences for 

society but may seem to be outside the typical scope of consideration by an organization’s AI designers. One reason 

is that many high-consequence risks would involve novel or low-probability events, or systemic risks, that could seem 

very unlikely or outside the scope of the organization's direct responsibility. Moreover, organizations have limited 

resources for risk identification and risk mitigation. Furthermore, guidance available on identifying and assessing lowprobability, high-consequence risks is likely less standardized and straightforward than typical guidance for 

identifying and assessing more common types of events (e.g., for standard information-system risk assessment). 

Thus, the RMF presents an opportunity for NIST to address these gaps and to guide organizations to consider risks of 

events with high consequences for society. The RMF also represents an opportunity within a voluntary framework to 

remind organizations of reasons why they should consider events with impacts to society, e.g., identifying risks to the 

organization’s reputation if an AI project becomes associated with undesirable societal-level outcomes. 

However, there are substantial challenges in addressing risks to society within a voluntary framework. Yeung (2021, 

p. 20) argues that such approaches as taken in the voluntary Privacy Framework may not be sufficient for the AI 

RMF: “Because [risks from use of AI systems] might cause physical harm or violate fundamental values, NIST should 

also incorporate more stringent elements in the AI risk management framework than were in the privacy 

framework.” As one way to address such challenges with voluntary frameworks, we suggest NIST consider 

coordinating guidance and other policy instruments including standards, at least for some domains. This could 

include collective proprietary attention to known risks, structured audits to help monitor poorly-understood domain 

dynamics, and/or certifications preceding deployment in high-risk settings. 

We recommend that the RMF provide guidance on risk identification, assessment and 

prioritization processes to include risks that could have high consequences for society but 

may seem to AI designers to be outside the typical scope of consideration for their 

organization, such as events that would be novel or low-probability events, or systemic 

risks, or expected to be outside their typical time horizon. 

An h h ll i i i d i h h d d d f h i AI bili i AI 

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; 

7 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

For definitions of AI safety (as well as reliability, robustness, security, and harmful outcomes from misuse), see AI 

safety research agendas and publications such as Amodei et al. (2016). 

Part of the work of safety is to build systems that remain under human control and are demonstrably subject to 

human oversight and periodic external evaluation. For one prominent example of technical work in this direction, 

see Hadfield-Menell et al. (2016). 

We suggest that NIST consider “assessment of generality” (i.e., assessment of the breadth of AI 

applicability/adaptability) as another important characteristic affecting trustworthiness of an AI system, or perhaps 

as a factor affecting one or more of the AI trustworthiness characteristics NIST has already outlined. If an AI has not 

undergone any assessment of its generality, that would suggest lower trustworthiness. If assessment indicates high 

generality of an AI, we expect it would be appropriate to conduct more in-depth risk assessment, more assessment 

of use cases beyond the originally intended use cases, longer time horizons in risk assessment, more continuing 

assessment, etc. (Ideally, a generality assessment process would be quick and low-cost for the majority of AI with 

low generality, while accurately identifying the smaller number of AI with high generality.) For discussion of AI 

generality, see e.g. Bommasani et al. (2021). 

For definitions of explainability, it is important to understand how the term has been used differently by various 

stakeholders and how in practice it has often failed to meet its objectives (Newman 2021). The definition of fairness 

is similarly contested (Mulligan et al. 2019). 

In h d fi i i f h l i bili i i i i l l i h di f k h ld 

We recommend that NIST consult with a diverse set of stakeholders, including risksensitive groups, for input such as on definitions of key terms to better understand how 

the terms have been used differently by various stakeholders. 

We also recommend that NIST consider “assessment of generality” (i.e. assessment of 

the breadth of AI applicability/adaptability) as another important characteristic affecting 

trustworthiness of an AI, or perhaps as a factor affecting one or more of the AI 

trustworthiness characteristics NIST has already outlined. 

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; 

8 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

Additional principles which should be considered are sustainability and inclusivity. For example, one of the OECD AI 

principles is, “AI should benefit people and the planet by driving inclusive growth, sustainable development and wellbeing.” Other AI risk and impact frameworks have also included these considerations (Yeung 2021). 

Over 170 sets of ethical AI guidelines have been developed (Algorithmwatch.org 2020). A growing consensus is 

emerging around the following principles: accountability, privacy and security, transparency and explainability, 

fairness and non-discrimination, professional responsibility, human control, and the promotion of human values such 

as civil and human rights. 

Organizations are taking concrete steps to operationalize AI principles. For example, the OECD Network of Experts 

on AI is creating a database of tools and practices to implement the OECD AI Principles (OECD 2021). For a more in 

depth case study on how organizations such as Microsoft are defining and managing AI principles, see Newman 

(2020). 

Finally, we recommend that NIST clarify two items in the RMF RFI regarding NIST’s use of the terms 

“characteristics” and “principles”. First, we recommend that the difference between principles and characteristics is 

made more clear. Second, where the RFI states that “These characteristics and principles are generally considered 

as contributing to the trustworthiness of AI technologies and systems, products, and services”, we recommend you 

clarify to what extent NIST meant “considered by the public”, or “considered by experts”, or both; differentiating 

expert and public evaluations of trustworthiness seems both descriptively salient and normatively appropriate. (This 

relates to RFI section Supplementary Information: Genesis for Development of the AI Risk Management 

Fr k ) 

We recommend that NIST consider including principles of sustainability and inclusivity. 

We also recommend that NIST clarify two items in the RMF RFI regarding NIST’s use of 

the terms “characteristics” and “principles”: 1. That the difference between principles 

and characteristics is made more clear, and 2. Where the RFI states that “These 

characteristics and principles are generally considered as contributing to the 

trustworthiness of AI technologies and systems, products, and services”, we recommend 

you clarify to what extent NIST meant “considered by the public”, or “considered by 

experts”, or both. 

Type: E - Editorial, G - General T - Technical 3 of 6 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19 

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; 

9 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

Research on organizational safety standards and the incorporation of AI technologies into the commercial aviation 

industry reveals how the opaque, unpredictable, and accident-prone nature of AI technologies results in slow 

adoption in safety critical domains. There is demand for collaborative AI safety standards that meet rather than 

relax aviation's high safety standards (Hunt 2020). 

References in this subsection: 

Hunt W (2020) The Flight to Safety-Critical AI: Lessons in AI Safety from the Aviation Industry. CLTC, 

https://cltc.berkeley.edu/wp-content/uploads/2020/08/Flight-to-Safety-Critical-AI.pdf 

5. Standards, frameworks, models, 10 (1): AI Security Anthony For effective risk identification, one best practice is to have risk identification processes performed by a team that is We recommend that NIST consider having the RMF include guidance to have risk 

methodologies, tools, guidelines and Initiative, Center Barrett (1), diverse, multidisciplinary, representing multiple departments of the organization, as well as including a identification processes performed by a team that is diverse, multidisciplinary, 

best practices, and principles to for Long-Term Thomas correspondingly diverse set of stakeholders from outside the organization. See, e.g., guidance on including representing multiple departments of the organization, as well as including a 

identify, assess, prioritize, mitigate, Cybersecurity, UC Krendl Gilbert stakeholders during project risk identification (PMI 2017, section 11.2), as well as guidance on the ranges of types of correspondingly diverse set of stakeholders from outside the organization. 

or communicate AI risk and whether Berkeley; (2): (2), Caroline stakeholders to include when identifying potential types of AI harm (Microsoft 2020). As we mentioned previously, 

any currently meet the minimum Center for Human- Jeanmaire (2), one proposal to manage risks more effectively, reliably, and safely is to incorporate feedback from stakeholders and We also recommend that the RMF include standardized templates for reporting 

attributes described above; Compatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

risk-sensitive groups, democratizing the structure of AI pipelines (Dobbe et al. 2021). The diversity of perspectives 

from such approaches can help identify a greater breadth and depth of risks that otherwise could be missed by a 

team without the same perspectives. 

It would be valuable for the Framework to include templates and definitions to facilitate information sharing on AI 

risk factors and incidents. Standardized tools for sharing information about incidents and risk factors could reduce 

costs and increase value of efforts to identify, assess, prioritize, mitigate, and communicate AI risk. For AI incident 

reporting, one leading effort is the Partnership on AI’s AI Incident Database (AIID n.d). Reporting on AI risk factors 

potentially could adapt procedures and templates currently used in the cybersecurity community for vulnerability 

disclosure. NIST could provide standardized reporting formats or other means to help AI developers share 

information in consistently beneficial ways. 

As mandated in the NDAA, NIST should align its efforts with international standards, as applicable. In doing so, NIST 

will support the development of standards that support greater efficiency and effectiveness in risk mitigation. We 

recommend that NIST review the work of the IEEE Ethics Certification Program for Autonomous and Intelligent 

Sy (ECPAIS) hi h i d l i ifi i i l di i d f dd i 

information on AI risk factors and incidents, that AI developers could adopt voluntarily. 

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; 

11 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

For a comparative analysis of AI risk and impact assessments from five regions around the world including Canada, 

New Zealand, Germany, the European Union, and San Francisco, California, see Yeung (2021). 

Please also see our discussion above of standards related to NIST AI RMF RFI topic #5. 

References in this subsection: 

Yeung LA (2021) Guidance for the Development of AI Risk and Impact Assessments, CLTC, 

https://cltc.berkeley.edu/2021/08/09/guidance-for-the-development-of-ai-risk-and-impact-assessments/ 

Type: E - Editorial, G - General T - Technical 4 of 6 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19 

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. 

12 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

Case studies documented in Newman (2020) detail how institutions including Microsoft and OpenAI have tried to 

improve the inclusiveness of AI design, development, use, and evaluation and also reduce and manage the risk of 

potential negative impacts. At Microsoft for example, the Responsible AI Program includes the AETHER Committee, 

the Office of Responsible AI, a Responsible AI Standard, and a Responsible AI Champs community. Microsoft 

researchers have also documented the role of checklists in AI ethics and worked on “harms modeling” designed to 

help researchers anticipate the potential for harm and identify gaps in products that could put people at risk 

(Madaio et al. 2020, Microsoft 2020). 

References in this subsection: 

Newman J (2020) Decision Points in AI Governance: Three Case Studies Explore Efforts to Operationalize AI 

Principles, CLTC, https://cltc.berkeley.edu/ai-decision-points/ 

Madaio M et al. (2020) Co-Designing Checklists to Understand Organizational Challenges and Opportunities around 

Fairness in AI, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 

https://dl.acm.org/doi/abs/10.1145/3313831.3376445 

Microsoft (2020) Foundations of assessing harm, Microsoft, https://docs.microsoft.com/enus/azure/architecture/guide/responsible-innovation/harms-modeling/ 

9. The appropriateness of the 

attributes NIST has developed for the 

AI Risk Management Framework. 

(See above, “AI RMF Development 

and Attributes”); 

13 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

While the RMF attributes list currently includes using plain language that is understandable by a broad audience, it 

does not explicitly include being user-friendly more broadly. Enabling ease of use for diverse stakeholders - for 

example by including implementation guides - is advised in order to help NIST achieve its goals for the AI RMF. 

We recommend that NIST consider adding usability as an attribute of the AI RMF. 

We recommend that NIST consider adding usability as an attribute of the AI RMF. 

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 

14 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

We commend NIST for planning to take an iterative approach with AI RMF development. We expect that 

appropriate, net-beneficial guidance addressing many key concepts (e.g., for some technical aspects of safety) may 

require more time to develop than would be feasible for inclusion in the initial Framework. 

We suggest that NIST consider clarifying its planned procedures for making RMF updates (how often, under what 

conditions, decision criteria), and how it aims to balance flexibility with standard-setting authority. 

For recommendations on linking the AI risk framework to procurement and purchasing decisions, see Yeung (2021). 

Yeung (2021, p.19) also discusses how the NIST Privacy Framework, as a voluntary framework, reminds organizations 

of reasons and incentives to consider risks affecting external stakeholders: "the framework points out how privacy 

risks can ... impact the organization, such as its reputation taking a hit or revenue loss from customers moving 

elsewhere. This linkage to organizational impact helps to provide parity between privacy risks and other risks that 

organizations are managing and leads to more informed decision-making." Similarly, the NIST Cybersecurity 

Framework also mentions that cybersecurity incidents can affect an organization’s reputation. However, Yeung 

(2021, p. 20) also argues that such approaches taken in the Privacy Framework may not be sufficient for the AI RMF: 

“Because [risks from use of AI systems] might cause physical harm or violate fundamental values, NIST should also 

incorporate more stringent elements in the AI risk management framework than were in the privacy framework.” 

Analytic dimensions of AI risks and possible domain manifestations are now being explored and mapped by technical 

and sociotechnical researchers. See Dean et al. (2021). 

We recommend that NIST consider clarifying its planned procedures for making RMF 

updates (how often, under what conditions, decision criteria), and how it aims to balance 

flexibility with standard-setting authority. 

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. 

Type: E - Editorial, G - General T - Technical 5 of 6 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19 

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. 

15 (1): AI Security 

Initiative, Center 

for Long-Term 

Cybersecurity, UC 

Berkeley; (2): 

Center for HumanCompatible AI, UC 

Berkeley; (3): 

CITRIS Policy Lab, 

CITRIS and the 

Banatao Institute, 

UC Berkeley 

Anthony 

Barrett (1), 

Thomas 

Krendl Gilbert 

(2), Caroline 

Jeanmaire (2), 

Jessica 

Newman (1), 

Brandie 

Nonnecke (3), 

Ifejesu 

Ogunleye (1) 

It would be very valuable for the Framework to include a comprehensive set of governance mechanisms to help 

organizations mitigate identified risks. These should include guidance for who should be responsible for 

implementing the Framework within each organization, ongoing monitoring and evaluation mechanisms that 

protect against evolving risks from continually learning AI systems, support for incident reporting, risk 

communication, complaint and redress mechanisms, independent auditing, and protection for whistleblowers, 

among other mechanisms. On auditing see, e.g., Raji et al. (2020); on AI incidents see the AI Incident Database 

(McGregor 2020) and Arnold and Toner (2021). We also recommend that the Framework encourage organizations 

to consider entirely avoiding AI systems that pose unacceptable risks to rights, values, or safety; related 

considerations are included in other AI risk frameworks (Yeung 2021). 

For an example of a leading AI enterprise that reviews applications that would use their AI platform, and disallows 

unacceptable categories of use cases, see OpenAI (2020). 

Assessment frameworks that address this include explorations of the problem of “trustworthy” mechanisms for 

verifying development claims and Z-inspection as a domain-specific approach to risk diagnostics. See Brundage et al. 

(2020) and Zicari et al. (2021). 

We recommend that NIST include guidance on governance processes to support the successful implementation of 

the AI RMF. We recommend reviewing Moss et al. (2021), which outlines “10 constitutive components” of 

supporting accountability in impact assessments. NIST should provide guidance on ways to support accountability in 

the implementation of the RMF (e.g., suggesting personnel/management levels that will implement and oversee the 

RMF ) 

We strongly recommend that the Framework include a comprehensive set of governance 

mechanisms to help organizations mitigate identified risks. These should include 

guidance for determining who should be responsible for implementing the Framework 

within each organization, ongoing monitoring and evaluation mechanisms that protect 

against evolving risks from continually learning AI systems, support for incident reporting, 

risk communication, complaint and redress mechanisms, independent auditing, and 

protection for whistleblowers, among other mechanisms. We also recommend that the 

Framework encourage organizations to consider entirely avoiding AI systems that pose 

unacceptable risks to rights, values, or safety. 

Type: E - Editorial, G - General T - Technical 6 of 6