SUBMITTED ELECTRONICALLY VIA REGULATIONS.GOV 

August 19, 2021

Subject: Artificial Intelligence Risk Management Framework [Docket Number: 210726-0151]

U.S. National Institute of Standards and Technology

MS 20899, 100 Bureau Drive

Gaithersburg, MD 20899

To Whom It May Concern: 

UL appreciates the opportunity to submit these comments to NIST on the Artificial Intelligence Risk 

Management Framework RFI. UL shares NIST’s belief that even as AI presents great opportunities for 

advancement of the US economy and society at large, it also presents risks and challenges to that same 

society. AI must comport with ethical values, norms and legal expectations of specific societies or 

cultures, be designed, developed, used, and evaluated in a trustworthy and responsible manner, and 

minimize harms to individuals, groups, communities, and societies at large.

Since its inception in 1894, UL serves a mission of promoting safe living and working environments for 

people everywhere and fulfills a promise of facilitating the flow of goods across borders. Grounded in 

science and collaboration, UL’s work empowers trust in pioneering technologies, from electricity to the 

internet. We help innovators create safer, more secure products and technologies to enable their safe 

adoption. Our nonprofit arm, Underwriters Laboratories, conducts rigorous independent research and 

analyzes safety data, convenes experts worldwide to address risks, shares knowledge through safety 

education and public outreach initiatives, and develops standards to guide safe commercialization of 

evolving technologies.

AI/machine learning (ML) enabled products present a unique challenge to product safety and safety 

certification. UL recognized this challenge immediately and has developed some practical elements of a 

risk management framework that may be relevant to NIST. These elements are now complete and their 

speedy introduction has helped facilitate a conversation with many stakeholders such as Automotive 

OEMs, startups and standards development organizations (SDOs), allowing us to continually to gather 

feedback and evolve our offerings. 

In 2020, UL issued the first version of the ANSI/UL 4600, Standard for Safety for Autonomous Products

(AP). The standard describes an approach that is a recognition of the challenge that AI/ML creates in 

bringing autonomous products to market. This standard follows a safety case assurance process as the 

usual prescriptive approach for product safety is not practical. The standard provides key elements that 

an autonomous products manufacturer must consider by gathering evidence to demonstrate the safety 

level of their product. Since the release of this first version, UL has provided formal training in UL 4600 

as part of its UL Certified Autonomous Safety Professional program. (UL Certified Autonomy Safety | 

Professional Training | UL 4600 (kvausa.com)). 
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In addition, this year UL has established a new service, the AI Algorithm Reproducibility Process 

Verification Mark. This offering allows manufacturers to demonstrate their willingness to have their 

algorithm process evaluated by an independent third party. Reproducibility is a key element of 

trustworthiness, and this offering helps fill a gap in the standards for AI trustworthiness. 

UL appreciates NIST’s statement in the RFI regarding a preference for existing standards, which is 

consistent with the principles of Office of Management and Budget (OMB) Circular A-119, and that the AI 

RMF be developed in an open and transparent process. This development has brought together a variety 

of stakeholders including software developers, sensor and radar manufacturers, ride share companies, 

car manufacturers, state and federal agencies as well as consumer advocacy groups. In addition to 

ANSI/UL4600, Underwriters Laboratories has published ANSI/UL 5500, Standard for Safety for Remote 

Software Updates. 

Please find below UL’s responses to a subset of the questions posed in the Request for Information. As 

NIST moves forward with its efforts to develop an AI Risk Management Framework, UL is eager to share 

our valuable expertise with NIST. If you have any questions regarding this submission or would like to 

discuss UL’s recommendations further, please do not hesitate to contact Thomas Daley, UL Global 

Government Affairs, at thomas.daley@ul.com. Thank you for your attention to these comments. 

Sincerely yours,

David S. Wroth

Director, Data Science

Underwriters Laboratories Inc.

QUESTIONS FROM NIST

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;

UL RESPONSE: Understanding the malleability of AI systems: AI systems are heavily reliant on training 

data, sensor/input data feedback (reinforcement learning) and other external input that can and do 

alter system behavior and performance. The literature is full of examples where AI systems produce 

unexpected results due to the difficulty in fully understanding the patterns developed through neural 

networks and machine learning approaches. One example of this is the use of adversarial techniques, 

including small physical changes to “known” objects to produce unexpected and harmful behavior. One

example is documented in “Robust Physical-World Attacks on Deep Learning Models.”1 Because systems 

may be more malleable than designers, users or managers recognize, it is difficult to fully understand 

1 Eykholt, et.al., Robust Physical-World Attacks on Deep Learning Models”, arXiv:1707.08945v5 [cs.CR]
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risk patterns. AI actors must consider “hard limits” to ensure AI systems don’t operate in harmful ways 

when the system encroaches on the boundaries of the operational design domain.

Understanding the risk associated with a “system of systems”: AI systems, complex in and of 

themselves, operate as part of other systems to be useful to society. Minimally, AI systems operate as 

part of a computer system, connected to a network system. Beyond that, the AI may be connected to

sensor systems and control systems. These interconnections may be a source of cyber vulnerability or a 

source of unanticipated feedback or action. Modeling the complexity of the systems and their 

permutations is a challenge that many AI actors may underestimate.

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;

UL RESPONSE: Context is an important characteristic of trustworthiness. While trustworthiness is 

normally a positive attribute, there are situations where ‘distrust’ may be valuable. Distrust in the form 

of healthy skepticism of an AI system may encourage higher levels of safety, security and privacy.

Defining the context of the system’s use and outcome may be an important element of the user’s ability 

to determine the level of trust put on the output of the AI. For example, in the medical field, the use of 

an AI in a diagnostic context requires high levels of trust by individuals and doctors to gain the benefits 

of the AI system. However, if an AI were used in defining a treatment program for a life-threatening 

condition, a level of ‘distrust’ would be useful to ensure corroboration by experienced doctors, similar to 

obtaining a “second opinion” which is common.

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;

UL RESPONSE: As covered in the introductory section of these comments, ANSI/UL 4600, Standard for 

Safety for the Evaluation of Autonomous Products, utilizes a “safety case” which is a structured 

explanation in the form of claims, supported by argument and evidence, that justifies that the item is 

acceptably safe for a defined operational design domain, and covers the item’s lifecycle. The safety case:

• leads designers through the thought process required to consider the possible complications the 

system may encounter;

• requires evidence the system is sufficiently robust to mitigate foreseeable hazards;

• Classifies criteria as falling into Mandatory, Required, Highly Recommended, or Recommended; 

and

• provides prompts of “have you considered,” examples and known pitfalls.
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Furthermore, ANSI/UL 4600 requires that the safety case is reviewed by an independent, knowledgeable 

individual for completeness and evidence. Section 8.5.6 of UL 4600 specifically addresses the use of AI in 

autonomous products.

This approach could be expanded to address other dimensions of trustworthiness, for example by a 

companion “privacy case” or “fairness case.”

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;

UL RESPONSE: AS NIST may be aware, ISO / IEC Joint Technical Committee 1, Subcommittee 42 on 

Artificial Intelligence has developed standardization in the area of Artificial Intelligence, including the 

focus and proponent for JTC 1's standardization program on Artificial Intelligence, and provides 

guidance to JTC 1, IEC, and ISO committees developing Artificial Intelligence applications. Recent 

standards developed by this committee include:

ISO/IEC TR 24028:2020 Information technology -- Artificial Intelligence -- Overview of trustworthiness in 

artificial intelligence

ISO/IEC TR 24029-1:2020 Artificial Intelligence -- Assessment of the robustness of neural networks —

Part 1: Overview

ISO/IEC TR 24029-1:2020 Information technology -- Artificial Intelligence -- Use cases

In addition to the work of ISO Joint Technical committee 1, standards in progress also include:

ISO/IEC 22989: Artificial Intelligence Concepts and Terminology

ISO/IEC 23053: Framework for Artificial Intelligence Systems Using Machine Learning

ISO/IEC 42001: Information technology -- Artificial Intelligence -- Management Systems

ISO/IEC 5259-1: Data quality for analytics and ML — Part 1: Overview, terminology, and examples

ISO/IEC 5259-2: Data quality for analytics and ML — Part 2: Data quality measures

ISO/IEC 5259-3: Data quality for analytics and ML — Part 3: Data quality management requirements and 

guidelines

ISO/IEC 5259-4: Data quality for analytics and ML — Part 4: Data quality process framework

ISO/IEC 24668: Information technology -- Artificial Intelligence -- Process management framework for 

Big data analytics

ISO/IEC Preliminary Work Item - Information technology – Artificial intelligence – Data life cycle 

framework

ISO/IEC TR 24027: Information technology -- Artificial Intelligence (AI) -- Bias in AI systems and AI aided 

decision making

ISO/IEC 24029-2: Artificial Intelligence (AI) -- Assessment of the robustness of neural networks -- Part 2: 

Formal methods methodology 

ISO/IEC 23894 -- Information technology -- Artificial intelligence -- Risk management
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ISO/IEC TR 24368: Information technology -- Artificial Intelligence (AI) -- Overview of Ethical and Societal 

Concerns

ISO/IEC TR 5469: Artificial Intelligence (AI) -- Functional Safety

ISO/IEC 25059 -- Software engineering -- Systems and software Quality Requirements and Evaluation 

(SQuaRE) – Quality Model for AI-based systems 

ISO/IEC TS 6254 -- Information technology -- Artificial intelligence -- Objectives and approaches for 

explainability of ML models and AI systems

ISO/IEC TS 5471 -- Artificial intelligence -- Quality evaluation guidelines for AI systems

ISO/IEC TR 24372: Information technology -- Artificial Intelligence (AI) -- Overview of computational 

approaches for AI systems

ISO/IEC TS 4213: Assessment of classification performance for machine learning models

ISO/IEC 5392: Information technology -- Artificial Intelligence (AI) -- Reference architecture of 

knowledge engineering

ISO/IEC AWI 38507 -- Information technology -- Governance of IT -- Governance implications of the use 

of artificial intelligence by organizations

NIST may consider joining the “Closing the Gaps in Responsible AI” effort by the Partnership on AI 

(www.partnershiponai.org). This initiative is a multiphase, multi-stakeholder project aimed at surfacing 

the collective wisdom of the community to identify salient challenges and evaluate potential solutions. 

These insights can in turn inform and empower the changemakers, activists, and policymakers working 

to develop and manifest responsible AI.