GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Passive Body Scanner
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: PBS is intended to enhance situational awareness in pedestrian traveler processing to aid  CBP officers in observing potentially dangerous objects or contraband in a timely manner and pursuant to CBP’s border search authority.

12_outputs: This algorithm highlights areas on a person where potential objects may be blocking the subject's expected body heat and displays these areas on live video image, monitored by a CBP officer. The highlighted areas may show the locations of carried objects, which could be potential weapons or contraband.

16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 8/18/2022
19_date_acq_dev_began: 9/29/2022
20_date_implemented: 9/29/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 70B03C22A00000031
24_hisp_support: No
27_public_info: No
29_contains_pii: No
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.
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: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Passive Body Scanner
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: These are stand-alone AI models that are provided as part of the equipment.  They do not touch IT infrastructure.  The systems are air gapped.
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.
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: Unmanned Aircraft Collision Avoidance (Skydio)
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 platform operates on video feed only which in turn activates the obstacle avoidance on the aircraft where the AI capabilities are housed.  The system supports the streamlined intake process while maintaining the accuracy and reliability of identity verification.
12_outputs: The pilot of the Skydio X2D will receive a visual alert on the hand controller, indicating a possible collision.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2022
19_date_acq_dev_began: 6/1/2022
20_date_implemented: 10/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Still capture pictures and video files.
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: Yes
41_system_name: Skydio X2D
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: CBP Translate
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 Translate is used to facilitate clear communication between officers and non-English-speaking individuals during border interactions. The AI enhances efficiency by reducing language barriers, supports diverse languages for greater inclusivity, and ensures accurate communication to minimize misunderstandings during critical procedures.

12_outputs: The system provides real-time translations of spoken or written communication, detects individuals' languages for accurate translation, and generates logs of interactions for review, streamlining processes and ensuring clear communication. This output is used to support necessary interpretation but sworn statements and official communications for law-enforcement purposes are moved to stardard translation services when necessary for formal documenation and questioning. 

16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

27_public_info: Yes
28_iqa_compliance: In progress, working as part compliance extension.
29_contains_pii: Yes
30_saop_review: Yes
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.
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: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: CBP Translate
42_dev_tools_wait: Less than 6 months
45_compute_request: Other
45_question_type: Working as part of compliance extension.
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: Entity Resolution
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: Detection research of force labor within supply chain utilizing analytical AI platform.
12_outputs: Detection of potential force labor within supply chain.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2021
19_date_acq_dev_began: 10/1/2021
20_date_implemented: 5/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 2014-14031000011
24_hisp_support: No
27_public_info: No
29_contains_pii: No
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.
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: Mobile Traveler Identity Verification
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 TVS Biometric matching service is a cloud-based facial biometric matching service that enables CBP to match a passenger’s identity against a trusted source, throughout the travel continuum which improves traveler facilitation and reduces manual identity verification
12_outputs: Provides a match or no match response
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2017
19_date_acq_dev_began: 9/1/2017
20_date_implemented: 1/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: 024-000005265
24_hisp_support: Yes
25_hisp_name: Customs and Border Protection
26_public_service:  Enabling trusted travelers to enter the country quickly with Global Entry (GE)
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Border Crossing Information/ Trusted Traveler Information
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Traveler Verification Service
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
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: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: Customs Broker License Exam - Proctor Support
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: CBP
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: The model supports remote proctoring of the exam and ensure the integrity of the testing process, by ensuring the exam is conducted under secure conditions, preventing cheating or fraud, while also verifying the identity of exam takers to confirm they meet the necessary requirements.

12_outputs: Integrity reports, identity confirmation, proctoring complicance feedback, and test results. 


16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2022
19_date_acq_dev_began: 10/2/2022
20_date_implemented: 3/31/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: N/A - Aligned with the Proctoring contract.
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.
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: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: ERNIE
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 enhances threat detection and prioritizes high-risk targets, improving operational efficiency and national security.
12_outputs: The model provides real-time risk assessments and alerts for potential threats detected by the Radiation Portal Monitors. It also provides prioritized recommendations for further screening based on the analysis of radiation data.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 10/1/2017
19_date_acq_dev_began: 10/1/2017
22_dev_method: Developed with contracting resources.
23_contract_piids: Developed under CWMD contract.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Numerical data from RPM radiation detectors and ERNIE assessments.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Automated Radiological Data Integration System - Cloud (ARDIS-C)
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
54_key_risks: This AI system has been tested in operational or real-world environments and risks have been identified and mitigated. Information about risks and mitigations for this use case may be law enforcement sensitive; DHS continues to review details for potential future disclosure in accordance with applicable law and policy.
55_independent_eval: Yes – by the CAIO
2_use_case_name: Autonomous Surveillance Tower (AST)
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: AST machine learning assisted system is augmenting the U.S. Border Patrol by enhancing the capabiltiies of individual users when carrying out the domain awareness mission. 
The expected benefit is to have ability of single person to monitor magnitude greater area than could be done with conventional CCTV or human surveillance. The ultimate outcome for the agency and the public is greater availability of the agents to solve and address more complex tasks and allow for better strategic/tactical deployment of existing resources and personnel.

12_outputs: The AI provides alerts when it detects the presence of an IoI (i.e., persons, vehicles, animals) in the image frame.  With regard to persons, this computer vision application is trained to determine if the object in the image frame is a person with a certain of level of confidence and not another object that may be shaped similarly to a person.  After the alert of a detection, a trained agent or user, reviews the image to identify and classify the activity taking place.  The AI merely alerts to the presence of an item it was trained to detect. This is not a biometric system and does not identify or track specific individuals. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2018
19_date_acq_dev_began: 10/1/2019
20_date_implemented: 1/1/2020
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 70B02C20D00000019
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Program created its own new data through new inputs either in real world or in real world simulated scenarios
33_agency_data: Meta-data and data created by CBP. Generally comprised of agent adjudications of autonomous sensory inputs of items of interests by the system.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Autonomous Surveillance Towers
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
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: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: Automated Item of Interest 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: The software analyzes images and video that are taken by operationally deployed equipment, which are then fed into CBP systems for review by USBP agents and personnel. It provides quick identification of people either crossing into the U.S. at a time and place other than designated for entry or those already inside the U.S. trying to elude capture, as well as the ability for human operators to quickly determine if subjects in an image are, in fact, human.
12_outputs: The system creates a layer which is overlaid over the image to produce a box around items of interest it has determined to be likely human beings.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
22_dev_method: Developed with contracting resources.
23_contract_piids: 70B06C24F00000475
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: All of the image data fed to the models are owned by USBP.
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: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: I4 Viewer
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
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: Cyber Threat Analysis (Recorded Future)
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: Cyber Threat Analysis quickly populates query results when searching against adversary tactics, techniques, and procedures, establishing a threat scorecard. This service can also provide cyber risk scorecards for third party vendors, companies, and organizations.
12_outputs: Actionable intelligence supporting Security Operations Center (SOC) and Cyber Risk Management (CRM) investigations and reports.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/1/2020
19_date_acq_dev_began: 4/1/2020
24_hisp_support: No
27_public_info: No
29_contains_pii: No
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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
43_infra_provisioned: No
45_compute_request: No
2_use_case_name: Vault Access Log (SPVAA)
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 system will enhance monitoring and minimizes the risk of unauthorized access, contributing to stronger security protocols for handling seized property.
12_outputs: Leverages DHS facial matching technologies to provide a match or no match response.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 12/1/2020
19_date_acq_dev_began: 1/1/2021
20_date_implemented: 8/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: 024-000005265
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: Personal Identity Verification (PIV) card
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Traveler Verification Service
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
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: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.