The Federal Executive Forum's Machine Learning and Artificial ...
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Generated: 10/13/2021
The Federal Executive Forum's Machine Learning and Artificial ...
The Federal Executive Forum’s Machine Learning and Artificial Intelligence Panel, in partnership with the Federal Research Division, has published a report titled “Machine Learning in Federal Digital Transformation.”
In the report, the panel presents an analysis of federal agencies’ use of machine learning algorithms, such as those that use natural language processing, as a way of solving challenges in public service through the use of data and analytics.
The publication draws from a survey of the Federal Research Division’s machine learning and artificial intelligence initiatives, a series of interviews with government and industry experts, and the authors’ experiences with machine learning in federal digital transformation.
The Federal Research Division is part of the Office of Research and Analysis of the Office of the Chief Human Capital Officer (OCHCO), within the Office of Personnel Management (OPM), where it is the government’s primary machine learning and artificial intelligence organization.
The Federal Research Division is currently exploring how to most effectively leverage machine learning technology for both policy and business transformation.
Key findings
The report includes the following key findings:
Federal agencies already have a presence of machine learning technologies in a variety of industries, providing real-world evidence of the technology’s effectiveness and value to commercial companies.
Agencies are considering the use of machine learning technologies for mission-critical applications within their organization in areas such as decision support, automated identification and assessment of vulnerabilities, anomaly detection, and automation in their workforces.
Agencies are investigating how to use a variety of machine-learning technologies, from general-purpose deep-learning networks to specialized algorithms for specific analytical tasks. For example, the Transportation Security Administration uses a deep-learning neural network to identify malicious images of air passengers.
For government agencies to successfully address their unique digital transformation challenges, they must consider the use of a range of machine learning technologies at appropriate levels of granularity—such as at the level of data, process or end user.
A central challenge to the widespread adoption of machine learning technologies in the federal workforce is ensuring that agencies can develop and maintain a high baseline of capability in key areas before introducing new technologies into the workaday processes of government agencies.
Agencies need to begin with an approach consistent with their organizational structure, mission and mission elements, budget and other key considerations.
FEDSNet and the Federal Research Division’s Machine Learning Division
The report explains the role the Federal Research Division’s Machine Learning Division plays within the Federal Information Technology Services (IT) organization and outlines how the division came to lead its work in machine learning.
Some of the key issues that the OPM’s Federal Technology Service (FTS) organization faced in 2017 included:
The importance of using machine learning for mission-critical capabilities within information technology (IT) functions
Potential security concerns related to machine learning for mission-critical workloads
The FTS is part of OPM, and the Federal Research Division has historically supported the mission of OPM. In 2017, however, the Federal Research Division was able to expand its mission to include machine learning and artificial intelligence, which provided a unique opportunity to work alongside the OPM IT organization to help advance new technologies within government.
Several themes emerged in the interviews that the Federal Research Division’s division conducted in advance of the machine learning report. These themes related to:
The importance, in the technology context, of fostering an understanding of the use of new technology and how to apply it within the context of the organization’s mission
The need for organizational buy-in as a key factor for the widespread integration of machine learning technologies into government
Several other common themes emerged while conducting the interviews, including the following:
The importance of data accessibility for the federal workforce and the need to build a data infrastructure
The importance of engaging with the public to raise awareness of specific uses of machine learning
The central role machine learning plays moving toward a technology-enabled public service
The need to have dedicated senior leaders to spearhead the advancement of machine learning and artificial intelligence
Related topics
Related topics
FEDSNet
FEDSNet stands for Federal Enterprise Data Standard for New Technologies and holds unique insight into federal enterprise standards for analytics, machine learning and Big Data use, and how the standard is evolving through the Open Source AI Lab and the Data Analytics Community of Practice.
The FEDSNet is governed by a Board of Directors that includes representatives from across the federal, industry and academic sectors.
The National Institute of Standards and Technology (NIST) is one of FEDSNet's founding members and sponsors, and provides technical guidance and leadership in a number of areas where open source analytics plays a critical role, such as:
Supporting NIST’s national efforts to leverage data analytics for science and security applications;
Contributing to the Federal Digital Service (FDS) Digital Analytics Framework, the FEDSNet Board of Directors includes representatives from the FDS, OPM, NASA, and NIST. The FDS has become the central point of convergence for information, data-driven decision making, and agile experimentation across all federal agencies.
The Data Analytics Community of Practice
The Data Analytics Community of Practice (CAP) is a community of practice in the federal space aimed at helping agencies build a common baseline of understanding, experience and capability in the use of analytics and data science technologies, along with enabling the development and sharing of best practices.
Over the last year, the CAP was established as a sub-group of the OPM’s Chief Human Capital Officer’s (OCHCO) Federal Data Center Community of Practice.
The CAP is a community of practice (i.e., a group of experts) to which agencies can belong for the following purposes:
To share their experiences in data and analytics use and capabilities with one another, and with the broader community of practice, in order to build mutual understanding and capabilities.
To discuss new innovations in data and analytics use and capabilities, and how they might enable federal enterprises to be more agile, more efficient and more effective.
To support the development, adoption and ongoing improvement of best practices.
Organizations of any size can become member to the CAP (see FAQ). The intent of this group is:
To encourage the sharing of best practices through educational offerings
To stimulate dialogue among members in order to develop common understandings of data and analytics use
To promote a sense of community among members
Who’s Who in Federal Data
Who’s Who in Federal Data
Who’s Who in Federal Data
Featured Members
Featured Members
FEDSNet:
The Federal Enterprise Data Standard for New Technologies (FEDSNet) is an organization managed by representatives from NIST to promote Federal Data Center standards for the use of analytics, Big Data and machine learning and artificial intelligence across the federal enterprise.
FEDSNet's primary activities include:
Developing standards for the use of analytics, Big Data and machine learning and artificial intelligence for the federal enterprise
Supporting the development and adoption of the Digital Analytics Framework, which is a standards-based approach to developing and using analytics, data-driven decision making, and agile experimentation at the enterprise level
Working with the FDS, the Department of Defense, NASA, the State Department, DHS, the Office of the President, USAID, the National Institute of Standards, the CIO Council, and other federal agencies to support the efforts of an all-domestic public cloud-in-a-box
Identifying and resolving problems in federal data and analytics use through the support of a community of practice
Providing training on federal data and analytics use
Who's Who in Federal Data
Who's Who in Data Analytics
The Federal Data Analytics Community of Practice is a community of practice aimed at helping agencies develop a common understanding of data and analytics use across a wide variety of data types and applications.
The Federation’s approach to data analytics is based on these principles:
Data analytics are essential to enable the achievement of mission, vision and mission objectives of the federal enterprise
Data analytics provide a new opportunity for effective operations for the federal enterprise
Data analytics capabilities, the approaches, products, methodologies and methods, must enable the achievement of these objectives
To realize the full value of data analytics, the use of information and the value of information must be integrated with the use of data analytics
The Federation advocates and promotes data analytics to advance these principles and objectives.
Featured Members
Who's Who in Data Analytics
Who's Who in Data Analytics
Featured Members
FEDSNet:
Federal Enterprise Data Standard for New Technology's primary function is to establish a set of standards for common use by agencies working to develop enterprise analytics and Big Data solutions.
FEDSNet has developed standards in partnership with other groups, such as the Digital Analytics Framework (DA Framework). FEDSNet is also a founding member of the Data Analytics Community of Practice, and supports the efforts of the Data Analytics Community of Practice. FEDSNet is also a member of ANGILE, which is a group of data analytics standards that are developed by various organizations throughout the federal government, both within the private sector as well as in the federal sector.
The Federal Executive Forum’s Machine Learning and Artificial Intelligence Panel, in partnership with the Federal Research Division, has published a report titled “Machine Learning in Federal Digital Transformation.”
In the report, the panel presents an analysis of federal agencies’ use of machine learning algorithms, such as those that use natural language processing, as a way of solving challenges in public service through the use of data and analytics.
The publication draws from a survey of the Federal Research Division’s machine learning and artificial intelligence initiatives, a series of interviews with government and industry experts, and the authors’ experiences with machine learning in federal digital transformation.
The Federal Research Division is part of the Office of Research and Analysis of the Office of the Chief Human Capital Officer (OCHCO), within the Office of Personnel Management (OPM), where it is the government’s primary machine learning and artificial intelligence organization.
The Federal Research Division is currently exploring how to most effectively leverage machine learning technology for both policy and business transformation.
Key findings
The report includes the following key findings:
Federal agencies already have a presence of machine learning technologies in a variety of industries, providing real-world evidence of the technology’s effectiveness and value to commercial companies.
Agencies are considering the use of machine learning technologies for mission-critical applications within their organization in areas such as decision support, automated identification and assessment of vulnerabilities, anomaly detection, and automation in their workforces.
Agencies are investigating how to use a variety of machine-learning technologies, from general-purpose deep-learning networks to specialized algorithms for specific analytical tasks. For example, the Transportation Security Administration uses a deep-learning neural network to identify malicious images of air passengers.
For government agencies to successfully address their unique digital transformation challenges, they must consider the use of a range of machine learning technologies at appropriate levels of granularity—such as at the level of data, process or end user.
A central challenge to the widespread adoption of machine learning technologies in the federal workforce is ensuring that agencies can develop and maintain a high baseline of capability in key areas before introducing new technologies into the workaday processes of government agencies.
Agencies need to begin with an approach consistent with their organizational structure, mission and mission elements, budget and other key considerations.
FEDSNet and the Federal Research Division’s Machine Learning Division
The report explains the role the Federal Research Division’s Machine Learning Division plays within the Federal Information Technology Services (IT) organization and outlines how the division came to lead its work in machine learning.
Some of the key issues that the OPM’s Federal Technology Service (FTS) organization faced in 2017 included:
The importance of using machine learning for mission-critical capabilities within information technology (IT) functions
Potential security concerns related to machine learning for mission-critical workloads
The FTS is part of OPM, and the Federal Research Division has historically supported the mission of OPM. In 2017, however, the Federal Research Division was able to expand its mission to include machine learning and artificial intelligence, which provided a unique opportunity to work alongside the OPM IT organization to help advance new technologies within government.
Several themes emerged in the interviews that the Federal Research Division’s division conducted in advance of the machine learning report. These themes related to:
The importance, in the technology context, of fostering an understanding of the use of new technology and how to apply it within the context of the organization’s mission
The need for organizational buy-in as a key factor for the widespread integration of machine learning technologies into government
Several other common themes emerged while conducting the interviews, including the following:
The importance of data accessibility for the federal workforce and the need to build a data infrastructure
The importance of engaging with the public to raise awareness of specific uses of machine learning
The central role machine learning plays moving toward a technology-enabled public service
The need to have dedicated senior leaders to spearhead the advancement of machine learning and artificial intelligence
Related topics
Related topics
FEDSNet
FEDSNet stands for Federal Enterprise Data Standard for New Technologies and holds unique insight into federal enterprise standards for analytics, machine learning and Big Data use, and how the standard is evolving through the Open Source AI Lab and the Data Analytics Community of Practice.
The FEDSNet is governed by a Board of Directors that includes representatives from across the federal, industry and academic sectors.
The National Institute of Standards and Technology (NIST) is one of FEDSNet's founding members and sponsors, and provides technical guidance and leadership in a number of areas where open source analytics plays a critical role, such as:
Supporting NIST’s national efforts to leverage data analytics for science and security applications;
Contributing to the Federal Digital Service (FDS) Digital Analytics Framework, the FEDSNet Board of Directors includes representatives from the FDS, OPM, NASA, and NIST. The FDS has become the central point of convergence for information, data-driven decision making, and agile experimentation across all federal agencies.
The Data Analytics Community of Practice
The Data Analytics Community of Practice (CAP) is a community of practice in the federal space aimed at helping agencies build a common baseline of understanding, experience and capability in the use of analytics and data science technologies, along with enabling the development and sharing of best practices.
Over the last year, the CAP was established as a sub-group of the OPM’s Chief Human Capital Officer’s (OCHCO) Federal Data Center Community of Practice.
The CAP is a community of practice (i.e., a group of experts) to which agencies can belong for the following purposes:
To share their experiences in data and analytics use and capabilities with one another, and with the broader community of practice, in order to build mutual understanding and capabilities.
To discuss new innovations in data and analytics use and capabilities, and how they might enable federal enterprises to be more agile, more efficient and more effective.
To support the development, adoption and ongoing improvement of best practices.
Organizations of any size can become member to the CAP (see FAQ). The intent of this group is:
To encourage the sharing of best practices through educational offerings
To stimulate dialogue among members in order to develop common understandings of data and analytics use
To promote a sense of community among members
Who’s Who in Federal Data
Who’s Who in Federal Data
Who’s Who in Federal Data
Featured Members
Featured Members
FEDSNet:
The Federal Enterprise Data Standard for New Technologies (FEDSNet) is an organization managed by representatives from NIST to promote Federal Data Center standards for the use of analytics, Big Data and machine learning and artificial intelligence across the federal enterprise.
FEDSNet's primary activities include:
Developing standards for the use of analytics, Big Data and machine learning and artificial intelligence for the federal enterprise
Supporting the development and adoption of the Digital Analytics Framework, which is a standards-based approach to developing and using analytics, data-driven decision making, and agile experimentation at the enterprise level
Working with the FDS, the Department of Defense, NASA, the State Department, DHS, the Office of the President, USAID, the National Institute of Standards, the CIO Council, and other federal agencies to support the efforts of an all-domestic public cloud-in-a-box
Identifying and resolving problems in federal data and analytics use through the support of a community of practice
Providing training on federal data and analytics use
Who's Who in Federal Data
Who's Who in Data Analytics
The Federal Data Analytics Community of Practice is a community of practice aimed at helping agencies develop a common understanding of data and analytics use across a wide variety of data types and applications.
The Federation’s approach to data analytics is based on these principles:
Data analytics are essential to enable the achievement of mission, vision and mission objectives of the federal enterprise
Data analytics provide a new opportunity for effective operations for the federal enterprise
Data analytics capabilities, the approaches, products, methodologies and methods, must enable the achievement of these objectives
To realize the full value of data analytics, the use of information and the value of information must be integrated with the use of data analytics
The Federation advocates and promotes data analytics to advance these principles and objectives.
Featured Members
Who's Who in Data Analytics
Who's Who in Data Analytics
Featured Members
FEDSNet:
Federal Enterprise Data Standard for New Technology's primary function is to establish a set of standards for common use by agencies working to develop enterprise analytics and Big Data solutions.
FEDSNet has developed standards in partnership with other groups, such as the Digital Analytics Framework (DA Framework). FEDSNet is also a founding member of the Data Analytics Community of Practice, and supports the efforts of the Data Analytics Community of Practice. FEDSNet is also a member of ANGILE, which is a group of data analytics standards that are developed by various organizations throughout the federal government, both within the private sector as well as in the federal sector.