AI and Open Government Data Assets Request for Information, 27411-27413 [2024-08168]

Agencies

• DEPARTMENT OF COMMERCE

[Federal Register Volume 89, Number 75 (Wednesday, April 17, 2024)]
[Notices]
[Pages 27411-27413]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2024-08168]


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DEPARTMENT OF COMMERCE

[Docket No. 240410-0103]
RIN 0690-XD001


AI and Open Government Data Assets Request for Information

ACTION: Notice, request for information.

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SUMMARY: The U.S. Department of Commerce is committed to advancing 
transparency, innovation, and the responsible use and dissemination of 
public data assets, including for use by data-driven AI technologies. 
To this end, we are pleased to issue this Request for Information (RFI) 
to seek valuable insights from industry experts, researchers, civil 
society organizations, and other members of the public on the 
development of AI-ready open data assets and data dissemination 
standards.

DATES: Comments must be received on or before July 16, 2024.

ADDRESSES: All electronic public comments on this action, identified by 
Regulations.gov docket number DOC-2024-0007, may be submitted through 
the Federal e-Rulemaking Portal at www.regulations.gov. The docket 
established for this request for comment can be found at 
www.regulations.gov, DOC-2024-0007. Click the ``Comment Now!'' icon, 
complete the required fields, and enter or attach your comments.

FOR FURTHER INFORMATION CONTACT: Please direct questions regarding this 
Notice to Victoria Houed at [email protected] with ``AI-Ready Open 
Data Assets RFI'' in the subject line, or if by mail, addressed to 
Victoria Houed, OUSEA, U.S. Department of Commerce, 1401 Constitution 
Avenue NW, Room 4848, Washington, DC 20230; telephone: (202) 913-1504.

SUPPLEMENTARY INFORMATION: The U.S. Department of Commerce (Commerce) 
is committed to leading the way in producing and disseminating high-
quality public data. Commerce's data assets enable U.S. scientific 
discovery, innovation, and economic growth, serving as an invaluable 
asset to the country. In its mission to publish data for the American 
public and achieve its strategic goal to ``expand opportunity and 
discovery through data,'' Commerce is dedicated to continuously 
refining its processes for creating, curating, and distributing its 
data as new technologies emerge. This Request for Information (RFI) 
seeks to understand ways to improve Commerce's creation, curation, and 
distribution of its open data assets to facilitate the development and 
advancement of AI technologies such as generative AI.
    Commerce, as a premier data provider, has a long history of 
adapting to technological change. In the past 40 years, Commerce has 
moved data publication efforts into electronic forms, and in the past 
20 years, that has included the provision of both data services and 
tools to support discovery and exploration of Commerce's data. In the 
last five years, Title II of the Foundations for Evidence-Based 
Policymaking Act, commonly known as the OPEN Government Data Act, began 
Commerce's commitment to the dissemination of open data assets in

[[Page 27412]]

machine-readable formats, or ``data in a format that can be easily 
processed by a computer without human intervention while ensuring no 
semantic meaning is lost'' (44 U.S.C. 3502(18)).
    Today, Commerce is facing a new technological change with the 
emergence of AI technologies that provide improved information and data 
access to users. Commerce is specifically interested in generative AI 
(GenAI) applications, which digest disparate sources of text, images, 
audio, video, and other types of information to produce new content. 
GenAI and other AI technologies present both opportunities and 
challenges for both data providers such as Commerce and data users 
including other government entities, industry, academia, and the 
American people.
    AI has brought transformative changes to many industries including 
health, finance, education, and transportation, while GenAI has the 
promise of democratizing access to data by enabling the average person 
to engage with data in ways that had not previously been possible. 
Recent GenAI tools allow users to input simple prompts to engage with 
content gathered by these tools from a wide range of sources, including 
Commerce's public data.
    The challenge for Commerce, as an authoritative provider of data, 
is to ensure that these new AI intermediaries can appropriately access 
its data without losing the integrity, including quality, of said data. 
AI tools require mass amounts of trustworthy information to accurately 
respond to the needs of their users. As AI applications become more 
sophisticated and ingrained in everyday life, the role of high-quality 
data becomes increasingly critical. Commerce acknowledges, as a key 
data producer, that in order for AI systems to utilize its data for 
training and for instant data retrieval, its data may need to be 
reconfigured in easily consumable formats. AI tools are increasingly 
used for data analysis and data access, so Commerce hopes to ensure 
that the data these tools consume is easily accessible and ``machine 
understandable,'' versus just ``machine readable.'' Therefore, this RFI 
explores how to achieve better data integrity, accessibility, and 
quality for emerging AI technologies.
    The uniqueness of emerging technologies such as GenAI arises from 
the fact that the interpretation and use of data is no longer solely 
executed by human experts (e.g., scientists, engineers, software 
developers) who bring their own knowledge and understanding to working 
with Commerce's data. This human understanding is grounded in shared 
disciplinary knowledge and in human-readable documentation that 
Commerce provides with its published data. AI systems currently lack 
common knowledge and the ability to use such knowledge in their 
activity. Although these systems demonstrate fluency and intelligence, 
their outputs are often driven by contextual prediction rather than 
higher-order reasoning capabilities. Recent AI systems are trained on 
tremendous amounts of digital content and generate responses based on 
the contextual properties of that content. However, these systems do 
not truly ``understand'' the texts in a meaningful way. While there is 
ongoing improvement, today's AI systems are fundamentally limited by 
their reliance on extensive, unstructured data stores, which depend on 
the underlying data rather than an ability to reason and make judgments 
based on comprehension. Knowing this, Commerce seeks to adhere to its 
strategic mission to ``expand opportunity and discovery through data,'' 
by disseminating public data in AI ready formats while ensuring no 
semantic meaning is lost.
    To respond to the challenge and realize the opportunity offered by 
these new technologies, it is important that Commerce enables AI 
systems to access and use its public data assets correctly and 
responsibly.
    This RFI seeks feedback, recommendations, and suggestions from 
industry experts, researchers, civil society organizations, and the 
public regarding Commerce's creation, curation, and distribution of 
data assets that are specifically designed to facilitate the 
development and advancement of AI technologies such as GenAI.
    Thus far, Commerce has made efforts to expose its public data 
through structured APIs and is developing enriched metadata standards 
for describing its data assets. To date, Commerce metadata has focused 
on enabling discovery of data assets rather than the use of those data 
assets by AI systems, but Commerce sees value in changing this focus. 
Commerce seeks to further understand how it can make its data assets 
AI-ready.
    In particular, Commerce wishes to explore the following:
     The use of knowledge graphs for variable level metadata, 
allowing systems to better link human terms to data elements;
     Embracing standardized ontologies such as schema.org or 
NIEM;
     Harmonizing and linking our internal ontologies and 
vocabularies using knowledge graphs grounded in standardized 
ontologies;
     Gathering internal and external written documentation of 
existing data products and:
    [cir] Mining them for terminology to use in metadata harmonization 
and linking; or
    [cir] Releasing them in raw formats for the training of AI models;
     Adopting data formats which allow for rich metadata as 
well as generating metadata ``sidecars'' for more traditional formats 
such as CSV or SAS;
     Using open standards for APIs with the ability to link 
into knowledge graphs; and
     Improving guidance and metadata around appropriate data 
usage and licensing for purposes such as research analytics, text-and-
data mining, and AI system ingestion.
    Commerce seeks comment on the topics discussed above and responses 
to the following questions:

Data Dissemination Standards

    1. What data dissemination standards should Commerce adopt to 
support human-readable and machine-understandable public data?
    2. What formats, metadata, and documentation should be prioritized 
to facilitate AI applications?
    3. How does raw data, such as data from the sensor networks, differ 
from derived data, such as statistical data from the U.S. Census 
Bureau, when it comes to metadata standards?
    4. What data licensing practices, standards, and usage 
considerations should Commerce consider to support broad, equitable, 
and open access to its datasets and metadata?
    5. What current standards exist or are under development that 
Commerce should consider to clearly signal that its public data is 
available for use by AI systems (or signal any accompanying conditions 
or restrictions on said data)?

Data Accessibility and Retrieval

    1. How can Commerce's data assets be made more accessible and 
valuable to the AI community (e.g., improved API access, web 
crawlability, etc.)?
    2. How can Commerce develop intuitive and accessible data portals 
that facilitate easy navigation and retrieval of data sets?
    3. What users should Commerce consider when disseminating our AI-
ready data? What atypical users should Commerce be sure to consider?
    4. What measures can be taken to encourage user-friendly 
interfaces, including clear labeling and readable

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formats, for Commerce's online data resources?
    5. How can Commerce better understand the needs of users for its 
data and the return on its investment in making its data more AI-ready?

Partnership Engagement

    1. How can industry and academic stakeholders collaborate with the 
government to shape the design and dissemination of AI-ready open data?
    2. What are the potential areas of partnership, and how can 
industry and academia contribute to enhancing data quality, integrity, 
and usefulness for AI purposes?

Data Integrity and Quality

    1. What are best practices that industries have employed to enhance 
the integrity and accuracy of public data when used in AI applications? 
What are best practices for data verification and validation? What are 
best practices for conducting regular audits and quality checks of data 
used in AI applications?
    2. How can we collectively address challenges related to 
authenticity bias, privacy, data quality, equity, and ethical use while 
maintaining transparency and accountability?
    3. What security protocols can be developed to mitigate risks of 
unauthorized data access and manipulation?
    4. How can Commerce promote transparency in data sourcing and 
processing methods to enhance trust and reliability? What is the 
expectation for reporting the quality of its data and how can we ensure 
that information will be carried through and presented to the end user?
    5. What validation processes can be established to maintain and 
verify data accuracy and consistency?
    6. How can Commerce facilitate comprehensive and transparent data 
documentation for replication and analysis?

Data Ethics

    1. What steps are needed to establish clear legal and ethical 
guidelines for AI data usage, ensuring privacy rights, preserving 
property rights, and focusing on equitable outcomes?
    2. What types of policies could Commerce implement to identify and 
mitigate biases in AI algorithms, including ensuring diverse data 
representation?
    3. What are the best protocols for ethical data collection, 
processing, and storage that prioritize data integrity and accuracy?
    Commerce invites your comments and insights on the above questions, 
as well as any additional input you deem relevant.

Oliver Wise,
Chief Data Officer, Department of Commerce.
[FR Doc. 2024-08168 Filed 4-16-24; 8:45 am]
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