AI Risk Management Framework

U.S. National Institute of Standards and Technology (NIST)

100 Bureau Drive

Gaithersburg, MD 20899

AIframework@nist.gov

September 28, 2022

Dear NIST Staff,

I am an independent researcher with an interest in artificial intelligence. I am excited by the work

NIST is doing with the AI Risk Management Framework (RMF or Framework) and Playbook, as

well as NIST’s call for comments on the second draft of the Framework. I recommend that NIST

consider revising the Framework to include two best practices: (1) use compute as a measure of AI

riskiness and (2) use data cards.

Use Compute as a Measure of Riskiness

On page 20, the RMF mentions that some applications of AI may be “deemed to be high-risk” but

the Framework does not define “high-risk.” This is unsurprising, as defining “high-risk” is extremely

difficult. But one cause of AI riskiness is their increased capabilities. For example the increased

scale of AI models increases the believability of content they generate, which will allow them to be

used in new applications that empower disinformation actors or harass individuals, in a manner

not possible before.

1 2 3

And while “riskiness” is subjective, capabilities are more quantifiable. As noted by OpenAI:

4

4 https://openai.com/blog/ai-and-compute/

3 https://arxiv.org/abs/2108.07258: “In addition to deceiving foreign populations, foundation models’ ability

to generate high quality synthetic images (deepfakes) or text may be abused to harass individuals.

Deepfakes have already been used for the purpose of harassment. For example, Rana Ayyub, an Indian

investigative journalist, was targeted by a high-quality deepfake that superimposed her face onto a

pornographic video, leading her to leave public life for months. Because foundation models are often

multimodal (§4.1: modeling), they could similarly impersonate speech, motions, or writing, and potentially

be misused to embarrass, intimidate, and extort victims.”

2 https://arxiv.org/abs/2108.07258: “Foundation models are capable of automatically generating much

higher-quality, human-looking content than prior AI methods. They may empower disinformation actors,

where states, for example, create content to deceive foreign populations without being transparent that

the content is linked to a state.”

1 https://arxiv.org/abs/2108.07258: “Advances in the scale (§4.2: training), multimodality (§4.1: modeling),

and adaptivity (§4.3: adaptation) of generative foundation models will allow them to be misused to

generate high-quality, cheap, and personalized content for harmful purposes.”
“Algorithmic innovation and data are difficult to track, but compute is unusually

quantifiable”.

“Improvements in compute have been a key component of AI progress, so as long as

this trend continues, it’s worth preparing for the implications of systems far outside

today’s capabilities.” (see Figure 1).

Figure 1: The Increasing Compute Usage of More Capable AI Systems

Source: https://openai.com/blog/ai-and-compute/

As a result, the Framework should recommend using the compute used to train the model, as one

measure of an AI model’s risk of misuse.

Use Data Cards

Data cards (also known as model cards or system cards) are a standardized system for providing

essential facts about AI datasets that are needed for responsible AI development.

5 According to

the US military’s Joint Artificial Intelligence Center (JAIC, now renamed to CDAO)

6

:

“The JAIC recently adopted the industry-proven use of data cards and has begun

developing the process workflow and framework that will support AI projects and

requirements across the Department”

6 https://www.ai.mil/blog_09_03_21_ai_enabling_ai_with_data_cards.html

5 https://arxiv.org/abs/2204.01075: “Data Cards are structured summaries of essential facts about various

aspects of ML datasets needed by stakeholders across a dataset's lifecycle for responsible AI

development.”
“The use of data cards is key to accelerating the adoption of safe and ethical AI into

DoD operations.”

While the Playbook does include two indirect references to data cards, I suggest that their use be

directly advocated and in the Framework.

Conclusion

Thank you for this opportunity to contribute to the Framework. I am happy to provide additional

details or information at the future workshop or other activities that NIST is using to develop the

Framework.

Best Regards,

Andre Barbe, PhD