STM (The International Association of Scientific, Technical and Medical Publishers)

www.stm-assoc.org

National AI Research Resource Task Force

National Institute of Standards and Technology

U.S. Department of Commerce

ai-bias@list.nist.gov

September 10, 2021

Comments on 

Draft NIST Special Publication 1270: 

A proposal for Identifying and Managing Bias in Artificial Intelligence

To the members of the National AI Research Resource Task Force:

STM, the International Association of Scientific, Technical and Medical Publishers, welcomes NIST’s efforts 

to support the development of trustworthy and responsible AI, and in particular the draft A Proposal for 

Identifying and Managing Bias in Artificial Intelligence (NIST Special Publication 1270). As STM and its 

members are highly engaged as both users, creators, and enablers of AI, we appreciate the opportunity 

to provide comments on the draft and would welcome any additional opportunities to engage with NIST 

on the important questions raised by AI and machine learning.

AI is highly relevant for academic publishers, with publishers engaged with AI in many ways, including: as 

key providers of high-quality content, context and data; as users of AI in internal workflows and services 

for authors, editors, and reviewers; and as users of AI in external tools and services. Being fully aware of 

the need to consider issues related to trustworthy and responsible AI, with respect to its potential but 

also its risks, STM published a White Paper in May 2021 outlining best practice principles for an ethical, 

trustworthy and human-centric AI. We feel that this White Paper overlaps significantly with the content 

and recommendations of the NIST draft proposal and may be helpful to inform NIST’s broader efforts on 

AI.

The points below provide comments on specific elements of the draft proposal for your consideration.

1. One goal of NIST’s work on trustworthy AI is the ‘development of a risk management framework 

and accompanying standards’ (line 167). STM agrees that the further development of AI should 

be guided by and grounded in clear legal standards and sound ethical principles. At the same 

time, the development of an AI-enabling policy framework that fosters the development of 

community-based standards should, where possible, build on existing initiatives. Due to the fastchanging nature of the AI landscape, legislators are urged to avoid inflexible legislative tools. We 

recommend a policy framework that respects intellectual property and continues to incentivize 

investment in high-quality content, datasets, and curated databases that can be used in AI 

applications, which are crucial elements for a trustworthy AI. The involvement of external 

experts with a broad range of expertise and experience in AI to review policy, and potentially, 

individual AI processes is essential. STM is committed to work with all stakeholders on next 

steps and would welcome the opportunity to support NIST’s efforts.
STM (The International Association of Scientific, Technical and Medical Publishers)

www.stm-assoc.org

2. We fully agree that bias is an inherent risk of AI (206). Paragraph 3.4 of the STM White Paper is 

devoted to this risk, with a specific focus on its potential harm on science. We support the 

recommendation made in the report that a variety of stakeholders should be engaged as AI 

systems and processes evolve, ensuring diversity in all areas (e.g., race, gender, age, physical 

ability, etc)

3. In section 2 (The Challenge Posed by Bias in AI System, 233-356) various factors are mentioned 

that are also listed in STM’s White Paper as essential elements to achieve trust in AI: reliable, 

unbiased and high-quality data, transparency on the level of algorithms and the application of AI 

technologies and systems; and the need to properly test AI. In the STM White Paper, a variety of 

suggestions are made to realize this:

a. data transparency by providing metadata on the origin, provenance, and validation 

process of data

b. an appropriate intellectual property framework to help incentivize the creation of highquality IP that can be used as input data, as well as to protect pre-existing IP that might 

be used to create, train, calibrate, repair, or improve AI systems;

c. Rigorous testing and evaluation, as well as the creation of feedback options for users, 

offered when they are made aware of the use of AI in tools, processes and services.

We recommend the addition of such suggestions to the NIST Proposal or reference to other 

sources, such as the STM White Paper. We kindly refer to the White Paper for more information.

4. We strongly support the three-stage approach described in section 4 to enable AI designers and 

deployers to better relate specific lifecycle processes with types of AI bias and facilitate its 

effective management. This resonates with our recommendation that a trustworthy, ethical, and 

human-centric AI depends on the careful selection of data, design and testing of algorithms and 

their deployment, its monitoring and auditing, as well as the provision of feedback mechanisms 

involving a wide variety of stakeholders. Although it may be beyond the scope of the Proposal, 

we believe that designers and deployers may need additional detailed suggestions for practical 

implementation, and recommend that NIST convene involve individual publishers, STM, and other 

AI experts to support the development of such implementation guidance.

We look forward to working with NIST as well as other stakeholders on identifying and managing bias in 

AI, as well as further develop principles and standards that address the other potential risks of AI. We 

would welcome any additional opportunities to support your efforts and share our views.

Very truly yours, 

Philip Carpenter

CEO
STM (The International Association of Scientific, Technical and Medical Publishers)

www.stm-assoc.org

About STM

At STM we support our members in their mission to advance trusted research worldwide. Our more than 

140 members collectively publish 66% of all journal articles and tens of thousands of monographs and 

reference works. As academic and professional publishers, learned societies, and university presses, startups and established players, we work together to serve society by developing standards and technology 

to ensure research is of high quality, trustworthy and easy to access. We promote the contribution that 

publishers make to innovation, openness and the sharing of knowledge and embrace change to support 

the growth and sustainability of the research ecosystem. As a common good, we provide data and analysis 

for all involved in the global activity of research.

The majority of our members are small businesses and not-for-profit organizations, who represent tens 

of thousands of publishing employees, editors, reviewers, researchers, authors, readers, and other 

professionals across the United States and world who regularly contribute to the advancement of science, 

learning, culture and innovation throughout the nation. They comprise the bulk of a $25 billion publishing 

industry that contributes significantly to the U.S. economy and enhances the U.S. balance of trade.