September 29, 2022 

SUBMITTED VIA ELECTRONIC MAIL at AIframework@nist.gov 

Elham Tabassi 

Chief of Staff, Information Technology Lab 

National Institute of Standards and Technology 

100 Bureau Drive 

Gaithersburg, Maryland 20899 

RE: Request for Comment on Artificial Intelligence Risk Management Framework 

Second Draft 

Dear Ms. Tabassi: 

The Alliance for Automotive Innovation (“Auto Innovators”) is pleased to submit 

comments to the National Institute of Standards and Technology (“NIST”) in response to its 

request for comments on the Artificial Intelligence Risk Management Framework (“AI RMF”) 

Second Draft. Auto Innovators appreciates that NIST continues to seek and incorporate feedback 

to support the AI RMF’s development.

Auto Innovators is the singular, authoritative, and respected voice of the automotive 

industry. Auto Innovators represents the manufacturers that produce nearly 98 percent of cars 

and light trucks sold in the United States, original equipment suppliers, technology companies, 

and other value-chain partners within the automotive ecosystem. Representing approximately 5.5 

percent of the country’s GDP and responsible for roughly 10 million jobs, the automotive

industry is the nation’s largest manufacturing sector.

The automotive industry leverages the power of artificial intelligence to integrate driver 

support features, advanced safety technologies, and automated driving systems into consumer 

vehicles. These and other technological advances have the potential to protect vulnerable road 

users, reduce serious injuries and deaths, improve roadway safety, and provide environmental 

benefits. To improve transportation safety through the deployment of technology, including 

artificial intelligence, we need to cultivate public trust and responsible technology usage and 

practice as part of a proactive, comprehensive, holistic, and collective approach to risk 

management within the relevant contextual environment. The automotive industry supports 

NIST’s clear intention that the AI RMF offer “approaches to minimize anticipated negative

impacts of AI systems and identify opportunities to maximize positive impacts (emphasis in 

original).”

Incorporating the feedback of Auto Innovators and other stakeholders, the Second Draft 

includes several important changes compared to the Initial Draft. For example, the explicit 

inclusion of “pro-innovation” in the proposed attributes of the AI RMF acknowledges the 

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ongoing evolution in artificial intelligence products, services, and systems over time. The 

expansion of the AI RMF’s intended audience to various artificial intelligence actors with their 

attendant artificial intelligence lifecycle activities reiterates the essentiality of “collective

responsibility” for successful artificial intelligence risk management. Auto Innovators 

appreciates the Second Draft’s inclusion of descriptions for each of the trustworthy artificial 

intelligence characteristics, their interrelationships, and their interdependencies; the reviews of 

how each characteristic may address potential artificial intelligence risks; and the guidance for 

addressing each characteristic. The additional details regarding the role of human factors in 

artificial intelligence risk management and oversight are also helpful. 

Specific Comments: 

As NIST continues its development of the AI RMF, Auto Innovators offers the following 

specific comments on the Second Draft: 

• Managing Bias: NIST states that three major categories of artificial intelligence bias –

systemic, computational, and human – must be considered and managed. Since artificial 

intelligence systems are comprised of both algorithms and data, it remains important to 

recognize that neither component is sufficient by itself to provide explainable or 

interpretable results. Balancing data sets to have output or target classes represented by 

the same number of input samples is important to present as many possibilities as 

possible. The AI RMF should encourage artificial intelligence actors, particularly in the 

design and development phases, to ensure training, validation, and holdout data sets are 

complete and then pair those data sets with algorithms to help facilitate bias-free 

outcomes. While training sets, in particular, are inputs that artificial intelligence actors 

can manage, it is only through the results of such pairing – the predictive outcomes – that 

success can be measured. 

• Safety: NIST notes that transportation is a field that could inform artificial intelligence 

safety measures. To that end, Auto Innovators reiterates its previously stated position that 

the AI RMF should not group safety risks that do not pose a potential risk of serious 

injury or death with potential safety risks that do pose such risks. As the Second Draft 

recognizes, “AI systems are not inherently bad or risky, and it is often the contextual 

environment that determines whether or not negative impact will occur.” Therefore, 

different types of safety risks may require tailored artificial intelligence risk management 

approaches based on context and the severity of potential risks presented. For example, 

“the ability to shut down or modify systems that deviate from intended or expected 

functionality” is not always possible in an automated vehicle or in some Advanced Driver 

Assist Systems (“ADAS”). Such a requirement could introduce unintended safety risks to 

the driver and other road users, particularly when the vehicle is in motion. Instead, the 

risk management best practice in this instance should be to reach a minimal risk 

condition. 

• Security: NIST includes the traditional cybersecurity triad of confidentiality, integrity, 

and availability, through protection mechanisms that prevent unauthorized access and 

use, to describe secure artificial intelligence systems. Auto Innovators recognizes that 
 

while these three attributes are often associated primarily with information technology 

(“IT”), they also apply to operational technology (“OT”). Nevertheless, we think it is 

important to specifically articulate OT-related security attributes such as safety, 

availability, and reliability in the discussion on secure and resilient artificial intelligence 

systems as well to communicate the applicability of the AI RMF to all artificial 

intelligence systems. 

• Transparency: Auto Innovators agrees with NIST that data used to build artificial 

intelligence systems may not be “a true or appropriate representation of the context or

intended use” of said systems. Sufficiently robust, diverse, and representative training 

data are critical to the safe, responsible, and trustworthy deployment of artificial 

intelligence, as well as to engender transparency with regards to the use of artificial 

intelligence systems. Transparency should also consider the human-machine interaction, 

e.g., how a human operator or user will be notified when an artificial intelligence system 

detects a potential or actual adversarial attack. In addition to considerations related to 

training data and human-machine interactions, NIST should expand its review of 

transparency to include the importance of reproducibility given the AI RMF identifies 

concerns about opacity as an artificial intelligence-specific risk. Therefore, Auto 

Innovators asserts that NIST should account for these and other factors in its discussion 

on transparency and accountability. 

Auto Innovators welcomes its ongoing collaboration with NIST on the development of 

the AI RMF. We appreciate NIST’s continued commitment to this multistakeholder, consensusdriven process. 

Sincerely, 

Tara Hairston 

Senior Director, Technology, Innovation, & Mobility Policy