GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Non-Intrusive Inspection (NII) 3D Imaging Tool
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Utilizes AI/ML to generate high resolution, rapid imaging of objects behind occlusions; create 3D images for existing processes without significant slowdowns; and provide a novel narcotics detection capability for the inspection of packages.
12_outputs: Detection alerts for Items of Interest.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 7/1/2023
19_date_acq_dev_began: 7/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 20139567
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: Images and data of baggage inspection.
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
2_use_case_name: Babel
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: CBP uses this tool to conduct targeted queries to aid CBP in open source research to monitor potential threats or dangers or identify travelers who may be subject to further inspection for violation of laws CBP is authorized to enforce or administer.

12_outputs: Babel utilizes AI modules for Text detection and translation as well as object and image recognition to provide analysts with possible matches to manually review in a single interface versus doing multiple manual queries. The output is not singly used for action or decision making and are used to identify additional Open Source or Social Media of a person or identify additional selectors (such as phone and emails) that are previously unknown to CBP and compared by an analyst against Government systems to identify additional derogatory information. These factors can often eliminate the traveler from additional screening.

16_dev_stage: Implementation and Assessment
17_impact_type: Rights-Impacting

18_date_initiated: 8/29/2023
19_date_acq_dev_began: 8/29/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 70B03C23F00001059
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: N/A - The Agency does not own any of the data used to train, fine-tune, and/or evaluate performance of the model(s) used in this use case.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
51_extension_request: Yes – Agency requested an extension for this use case.
52_impact_assessment: Planned or in-progress.
54_key_risks: Forthcoming. Office of Management and Budget (OMB) approved a compliance extension through November 30, 2025.
2_use_case_name: Fivecast ONYX
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The system enhances CBP's capability to monitor, analyze, and assess threats related to border security by processing vast amounts of open-source data, CBP aims to detect potential risks, monitor emerging trends, and uncover connections between individuals, organizations, or networks involved in illegal activities such as human trafficking, smuggling, or terrorism. Thereby streamlining operations and bolster security measures while maintaining a proactive approach to emerging threats

12_outputs: The system  aggregates vast amounts of publicly and commercially available data from diverse sources, such as social media, news streams, company records, and even the dark web, to provide insights on potential threats and risks, that can be used as actionable intelligence reports, risk assessments, and alerts that support CBP personnel  in real-time decision-making and long-term strategic planning. 

16_dev_stage: Implementation and Assessment
17_impact_type: Rights-Impacting

18_date_initiated: 8/29/2023
19_date_acq_dev_began: 8/29/2023
21_date_retired: 12/31/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: N/A - The Agency does not own any of the data used to train, fine-tune, and/or evaluate performance of the model(s) used in this use case.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
51_extension_request: Yes – Agency requested an extension for this use case.
52_impact_assessment: Planned or in-progress.
54_key_risks: Forthcoming. Office of Management and Budget (OMB) approved a compliance extension through November 30, 2025.
2_use_case_name: Public Information Compilation for Travel  Threat Analysis (Dataminr)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: This tool significantly reduces the amount of time it takes for users to collect and compile commercially available open-source information when attempting to identify  possible threats related to national security, border violence, CBP facilities, CBP employee safety and other topics with a CBP-nexus involving air, sea, and land travel to and/or from the U.S.

12_outputs: The AI output is compiled publicly available information for awareness.  CBP employees further research the information, including reading the source information, to determine if there is a possible threat.  

16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 9/15/2023
19_date_acq_dev_began: 9/15/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 70B03C23F00001059
24_hisp_support: No
2_use_case_name: Anomaly Detection in Non-Intrusive Inspection
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The model will identify irregularities or deviations from expected patterns that may indicate concealed contraband or threats while reducing overall review time.

12_outputs: Specific areas within scanned images that show anomalies, possibly needing further screening.

16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: Advanced Analytics for X-ray Images (AAXI)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: AAXI aims to address the problem of anomaly detection in empty commercial vehicles entering the United States at land border ports of entry. The AI models achieve this goal by encoding past X-Ray images of vehicular border crossings in a semantically meaningful way and comparing the current crossing to detect differences amongst the images to identify anomalies. Benefits include enhancement of the capability of humans to consistently detect items of interest/concern present (and possibly concealed) in vehicles crossing into the United States, and increased clearance rate at border crossings so that vehicles operating safely and lawfully may pass through the border faster.
12_outputs: AAXI produces bounding boxes around an anomaly or unidentifiable object(s) within an image or any portion of the image that cannot be identified or explained.
16_dev_stage: Acquisition and/or Development
17_impact_type: Rights-Impacting

18_date_initiated: 1/1/2022
19_date_acq_dev_began: 6/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: IAA with S&T
24_hisp_support: No
31_data_catalog: No
33_agency_data: NII Images
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
40_has_ato: No
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
2_use_case_name: Automated Data Annotation
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The Automated Data Annotation system is used to manage training data, collaborate on internal annotation strategies, and to process vast amounts of raw data for AI/ML applications collected from several CBP missions.The intended purpose of the AI is used to generate domain specific training data to facilitate model training for specific mission use cases. The expected benefit would be increased accuracy and confidence in model development.
12_outputs: High-quality, labeled datasets in JSON format ready for machine learning. It also provides metadata, including annotation metrics and quality insights, to ensure accuracy and support model training workflows.

16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/13/2024
2_use_case_name: AI Enabled Autonomous Underwater Vehicle
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Customs and Border Patrol (CBP) Office of Field Operations (OFO) has identified significant potential for parasitic loads on vessels entering and exiting ports of entry. The current identification method is using dive teams or borrowing larger ROV units from Local, State, or Federal partners. Through autonomous systems, OFO can more efficiently and safely identify anomalies/items of interest. 

12_outputs: This AI helps a small submarine drone detect possible contraband or safety risks to prioritize additional checks by human divers.


The technology allows for increased shared situational awareness in real time for OFO and strategic partners and improves mission planning and agent and officer safety, while reducing reactionary gaps. 
[this needs to be more clear about the ai system outputs - is the ai outputting suspected loads indicators? imagery? the closed loop fully autonomous navigation implies other self-looping outputs for navigation on obstacles and currents, are there any others?]
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2024
2_use_case_name: Relocatable Multi-Sensor System
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The system uses advanced sensor technology to differentiate valid items of interest (IOI), such as unmanned aircraft systems and humans, from other detections such as animals or other environmental objects. By integrating radar and other sensor data, the system filters out false alarms, ensuring more accurate identification of potential IOI. This capability enhances CBP's ability to focus on legitimate risks while minimizing the time spent on non-threatening activities, improving operational efficiency at border and security checkpoints.

12_outputs: The outputs  include real-time data identifying and categorizing potential items of interests, while filtering out false or non-relevant items of interest like animals. These outputs are used to provide situational awareness and support decision-making for CBP personnel. 

16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/24/2023
2_use_case_name: AI/LLM to generate testable synthetic data
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of using AI to generate test data for trade partners is to create more realistic data for trade partners to use for testing their systems before releasing new ACE capabilities.  Currently there are many data issues where test data does not accurately reflect real production data which results in unrealistic failures when testing which result in wasted time and resources tracking down false positive errors during testing.  By providing trade partners with more realistic test data the expectation is that testing times will be shorter and enhancements and capabilities can be delivered quicker.
12_outputs: The AI capability would generate test data without PII or other trade sensitive data and allow for more accurate simulation of production data.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/1/2023
2_use_case_name: Anomaly Detection COV Structure
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Anomaly Detection COV Structure is an AI algorithm solution to assist CBP officers in adjudicating  large-scale Non-Intrusive Inspection (NII) x-ray images. The solution will identify regions of interest within the structure of Commercially Owned Vehicles (COV) and detect anomalies within these regions. The algorithm will support the review and analysis conducted by image analysts during NII x-ray image adjudication ultimately reducing overall review time for NII images.
12_outputs: Bounding boxes around an anomaly or unidentifiable object(s) within an image or any portion of the image that cannot be identified or explained.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/26/2023
2_use_case_name: Anomaly Detection Homogenous Cargo
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Anomaly Detection Homogenous Cargo is an AI algorithm solution to assist CBP officers in adjudicating large scale Non-intrusive Inspection (NII) x-ray images. The solution will identify anomalies within homogenous cargo contained in commercially owned vehicles (COVs). The algorithm will support the review and analysis conducted by CBP image analysts during NII x-ray image adjudication ultimately reducing overall review time for NII images.
12_outputs: Bounding boxes around an anomaly or unidentifiable object(s) within an image or any portion of the image that cannot be identified or explained.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/26/2023
2_use_case_name: Anomaly Detection POV Structure
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Anomaly Detection POV Structure is an AI algorithm solution to assist CBP officers in adjudicating large-scale Non-Intrusive Inspection (NII) x-ray images. The solution will identify regions of interest within the structure of Privately Owned Vehicles (POV) and detect anomalies within these regions. The algorithm will support the review and analysis conducted by image analysts during NII x-ray image adjudication  ultimately reducing overall review time for NII images while facilitating legitimate trade and travel.
12_outputs: Bounding boxes around an anomaly or unidentifiable object(s) within an image or any portion of the image that cannot be identified or explained.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/26/2023
2_use_case_name: CBP Careers Bot - Leo
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Visitors to the U.S. Customs and Border Protection (CBP) careers website can engage with a decision-tree based chat bot to help access CBP career related information and drive users to take the next action such as contacting a recruiter, attending a career event, or applying for a CBP career to help access CBP career related information and drive users to take the next action. The addition of Natural Language Processing (NLP)-driven responses will allow for more natural, conversational interactions to increase usability and accuracy in provided information.
12_outputs: The AI-chatbot will provide natural language responses to user queries.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2023
2_use_case_name: Computer Vision for Aerial Detection of Land and Open Water Items of Interest
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: This capability will automate cross-border maritime threat detection, classification and tracking, enabling more efficient maritime border security surveillance.   
12_outputs: This capabilty will provide alerts and tracks of detected/classified maritime threats to system operators on a workstation user interface.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2024
2_use_case_name: AI for Software Delivery
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The initial use case is to integrate AI into the development process to assist developers with code reviews so that when a request to modify code in a baseline is made by a developer AI is integrated into the continuous integration pipeline to examine the code for potential problems and inefficiencies.  By reducing code review time and identifying potential issues earlier in the development CSPD expects to reduce the time to deliver changes and increase initial software quality.
12_outputs: The AI model will identify coding errors and recommend fixes as well as make recommendations for improvement and optimization which the request reviewer will assess and determine what, if any, changes need to be made before the code is approved to go into the baseline.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/1/2023
2_use_case_name: Optical Counter - UAS Detection
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: This capability will automate cross-border air & ground threat detection, classification and tracking, enabling more efficient border surveillance.
12_outputs: This capabilty will provide alerts and tracks of detected/classified air & ground threats to system operators on a workstation user interface.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 4/1/2023
2_use_case_name: Thermal Power Generation with Geoseismic IoI Detection and Classification
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Utilize seismic sensor data to determine Item of Interest traffic in deployed locations. Increases situational awareness in austere environments and reduces need for battery replacement since the devices are self-charging.
12_outputs: Alert that an Item of Interest (IOI) is present and the confidence interval that the detected seismic activity is correctly classified.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 3/17/2021
2_use_case_name: Underwater ROV
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The expected benefit of the system is that users will be able to more quickly identify potential threats and contraband on maritime vessels. This identification will allow for the streamlined investigations of a ship's hull without the need for a dive team.
12_outputs: The AI system outputs provide an informed decision on if a potential threat or contraband is present on a maritime vessel hull.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2024
2_use_case_name: Wellness and Physical Fitness Application 
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: This is an internal  AI powered personal workout coach for Customs and Border Patrol Staff. 

Wellness and Physical Fitness Application uses AI to provide increased user awareness of physical status and associated remedial actions, if necessary. Health and wellness awareness intends to mitigate human capital costs.

12_outputs: Personalized physical fitness assessments and programs with real-time AI fitness monitoring and program adjustments. Metrics, graphs, and display for group and/or individual trends analyses. 

16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2022
2_use_case_name: API Security Vulnerability Technology
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: With the necessity of leveraging application programing interfaces (APIs) for applications across the enterprise, this AI technology is intended to run thousands of custom attack scenarios against APIs on a continuous basis. This will help to identify potential security vulnerabilities prior to production level deployments, and enable the enterprise to develop essential remediations, if required.  Additionally, the techonlogy is intended to provide continuous monitoring on APIs to provide real-time alerts on new potential vulnerabilities.

12_outputs: The AI system is intended to output custom reports on identified vulnerabilities within application programing interfaces (APIs). These reports will include the identified risk and a summary of the risk, as well as tailored remediation guidance based on the applications, environment, data, and tests conducted for end-users.


16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/19/2024
2_use_case_name: Cyber Threat Detection
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The technology will provide premise and/or behind the firewall deployments to support all cloud environments. The technology will establish a decoy file sharing capability such as SharePoint, Shared Drive, etc. with tailored decoy products or reports and monitor and alert on the access and interactions with the decoys.

12_outputs: The technology enables the accurate detection of malicious activity in the cyber environment for actionable insights so that leadership can identify threats and efficiently mitigate them while reducing the cost of other security defenses. With the technology, the user will be alerted of detected compromise, evaluation of the creation of decoys, and the monitoring/analysis of interaction with the decoys from the adversary.

16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 3/29/2024