GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Contract Requirement Automation
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Eliminates the manual burden and reduces errors in document creation, where staff previously spent excessive time writing requirements to meet specific procurement needs from scratch. Increases documentation accuracy, consistency and standardization. Improves management and verification of requirement documents. Cost savings represent another major benefit, as the platform reduces labor costs associated with manual document creation and management. By streamlining the procurement process, the agency can complete more procurement actions with existing resources, maximizing taxpayer dollars. The automated tool also reduces the time spent on repetitive tasks, allowing for better resource allocation.
12_outputs: The tool primarily outputs automated document generation and recommendations for procurement documentation. The platform provides contextual recommendations during the document creation process to ensure completeness and compliance with procurement requirements. The tool provides recommendations and decision support to guide users through the proper documentation structure and content requirements.
The system's outputs always require human verification as part of the workflow, ensuring that all generated content is reviewed and approved by qualified personnel before being finalized in the procurement process.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 4/1/2024
2_use_case_name: TSA Case Handling Platform
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The tool saves case workers manual time to download reports, order, and compile. The case workers can then use their time to work on cases rather than administrative tasks. Newton POC has the potential of streamlining collection processes related to cases, and creating custom reports from various materials during the case managers interview process, producing a centralized tool to manage and control all steps within each case.
12_outputs: The LLM automation capabilities include compiling, ordering, and exporting a PDF document that contains 6 key documents from the initial steps of formal complaint process, and 3 key documents at the final stages of the complaint.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
2_use_case_name: CDC Airport Hotspot Throughput (PageRank)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
10_commercial_ai: None of the above.
11_purpose_benefits: TSA launched the “Stay Healthy. Stay Secure.” campaign, which details proactive and protective measures implemented at security checkpoints to make the screening process safer for passengers and our workforce by reducing the potential of exposure to the coronavirus. The campaign includes guidance and resources to help passengers prepare for the security screening process in the COVID environment. A big part of that campaign was the development of the Centers for Disease Control and Prevention's Airport Hotspot Throughput. This capability determines the domestic airports that have the highest rank of connecting flights during the holiday travel season to help mitigate the spread of COVID-19. This capability is a DHS-developed artificial intelligence model written in Spark/Scala that takes historical non-PII travel data and computes the highest-ranking airports based on the PageRank algorithm. TSA does not make decisions about flight cancellations or airport closures. These decisions are made locally, on a case-by-case basis, by individual airlines, airports, and public health officials. TSA will continuously evaluate and adapt procedures and policies to keep the public and our workforce safe as we learn more about this devastating disease and how it spreads.
12_outputs: This use case is inactive. Please see the "intended purpose and expected benefits" for a full description of this use case.

16_dev_stage: Retired
21_date_retired: 10/1/2024
2_use_case_name: Airport Throughput Predictive Model
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: This project was to create a predictive model for the passenger volume using the Security Operations throughput count from checkpoints to help with airport staffing.
12_outputs: Once a month the data is ingested, the predictive model is trained, and predictions of airport checkpoint throughput are made for the airports.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2023
19_date_acq_dev_began: 6/1/2023
20_date_implemented: 4/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: Performance Engineering Analytics – Contract#:47QRAD20D1175, Task Order:70T02023F7500N001
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: Secure Flight Passenger Data: passenger and airline reservation information received from airlines; PMIS Data: Secure checkpoint throughput counts by airport and checkpoint
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: Secure Flight
42_dev_tools_wait: More than 12 months
43_infra_provisioned: No
45_compute_request: Other
45_question_type: Performance Engineering has an on-prem environment with machine learning/modeling capabilities
47_timely_resources: Other
47_question_type: N/A - On-Prem
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: Silicon Valley Innovation Program (SVIP) Language Translator
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCG
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: A handheld voice translator could be used to communicate the questioning of vessels in high-tension situations where a USCG vessel does not have a translator available, or they encounter different or multiple languages. In cases where a rescue is involved, being able to determine critical information such as the number of people aboard and status of life saving equipment is vital to commanders in their decision-making process. Once the USCG boarding team makes contact with the occupants of the boarded vessel, they must be able to communicate and give orders to the occupants of the vessel who may not speak English or in many cases speak multiple languages. The development of an offline language translator could enable accurate and swift translation of orders and directions which are critical to the safety and security of the boarding team and the occupants of the vessel.
12_outputs: Real time language translation.
16_dev_stage: Retired
21_date_retired: N/A - Active as R&D only.
2_use_case_name: Adaptive Risk Model for Inspected Small Passenger Vessels
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCG
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The Small Passenger Vessels (SPV) Safety Task Force leveraged machine learning and subject matter expert input to create an adaptable, quantitative analysis tool that reliably identifies the key drivers of marine casualties and calculates a risk score for each vessel within the largest segment of the U.S.-inspected fleet. A vessel risk score is computed with an analytic model which leverages logistic regression and basic machine learning. The effort directly improved marine inspector allocation, targeted risk and enhanced the governance necessary to increase passenger safety across the country.
12_outputs: Numerical score that compares vessels predicted safety risk relative to each other.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 11/1/2019
19_date_acq_dev_began: 11/1/2019
20_date_implemented: 1/1/2021
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: Yes
33_agency_data: Commercial vessel profiles including: engineering, life saving, propulsion, fire protection, manning, operating routes, plan review, and USCG inspection activity details.
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: No
42_dev_tools_wait: Less than 6 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: 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: Intelligent Document Processing (IDP) for I-539 Form Digitization
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: IDP for the I-539 makes use of an AI-enhanced tool to identify, categorize, and create separate images for each document type submitted as part of the 539 benefit application. Prior to implementation of this use case, all pages of a 539 application were scanned and stored as a single document in the content management system.  The benefit is reduced case processing time for adjudicators by identifying and classifying supporting documents for ease of use. An additional benefit is to bring digital images into compliance with NARA standards.
12_outputs: Input - one digital file comprised of all pages of a 539 benefit application. Output - multiple digital files comprised of the individual documents submitted as part of the 539 benefit application. These will include the 539 form, any other USCIS forms, and image files of other supporting documents such as Passports, Driver's license, Marriage Certificate, Bank Statement, etc. All pages of the original digital file are accounted for and stored. Any pages not identified by the tool are referred to a human for document type resolution.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 9/30/2023
19_date_acq_dev_began: 10/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 70SBUR23Q00000186
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Other
31_question_type: Model development is done with test data and input from adjudication Subject Matter Experts to identify form types
33_agency_data: CMS/STACKS test data is used to train the model. This data is comprised of digital images of blank USCIS forms and common supporting forms (Marriage License, Driver's License, Passport, ect.) generated using fake information such as Mickey Mouse and Donald Duck in place of PII.
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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Content Management Services (CMS)
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: 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: Large Language Models for an Officer Training Tool
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The LLM for an Officer Training tool, will use GenAI to improve the way the agency trains immigration officer personnel. The tool will generate dynamic, personalized training materials that adapt to officers’ specific needs and ensure the best possible knowledge and training on a wide range of current policies and laws relevant to their jobs.
12_outputs: The AI outputs human-like conversation in a text format, similar to other AI chatbots, but specially trained and tuned for RAIO Officer training.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 10/20/2023
19_date_acq_dev_began: 11/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 70SBUR25F00000003
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: The USCIS Officer Training Tool does use Proprietary/Private but Not Sensitive data, including training materials, internal guidance documents, and policies that are proprietary to the USCIS or the training program fall under this category. While not publicly accessible, this data is not considered sensitive but should still be treated with a degree of confidentiality to maintain the integrity of the training program.
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: No
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 published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: User Entity and Behavior Analytics (UEBA) for Security Operations (SecOps) Anomaly Identification
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: UEBA's purpose is to review USCIS system logs to determine when an entity is performing actions that are anomalous. An entity can be classified as a workstation, server or an internal USCIS system account. The UEBA ingests logs from systems to perform analytics based off of models that are manually created and maintained. UEBA uses the models to apply a risk score to the entity which the risk score is then used to create a case (or ticket) for Security Operations analyst review. The AI reviews the action of the analyst to adjust the risk scoring for future events. Output would assist in prioritizing cyber events for further manual investigation.
12_outputs: Output of the Machine Learning is an alert with all artifacts for the SOC to investigate. The alert is used as a recommendation to prioritize specific investigations in the SOC ticket queue.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/18/2023
19_date_acq_dev_began: 8/18/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 70SBUR24F00000168
24_hisp_support: No
31_data_catalog: No
33_agency_data: Data used to tune models is USCIS internal system logs.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
40_has_ato: Yes
41_system_name: ESS
42_dev_tools_wait: 6-12 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: 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: I-765 - USCIS Face Capture Mobile App
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: This will allow the user to complete the biometric verification requirement without having to attend an appointment at am Applicant Support Center. This reduces the burden on the beneficiary as well as reducing demands on USCIS Applicant Service Center resources.
12_outputs: Face detection locates human faces in visual media such as digital images or video. When a face is detected it has an associated position, size, and orientation; and it can be searched for landmarks such as the eyes and nose which are returned as numerical values. 

ELIS Photo Validation service sends response back to user based on the pre-defined quality checks if the uploaded photo meets USCIS requirements. I-765 - USCIS Facial Recognition through IDENT (1:1 Face Recognition/Validation) returns a match or no match response from IDENT.
16_dev_stage: Acquisition and/or Development
17_impact_type: Rights-Impacting

18_date_initiated: 10/1/2023
19_date_acq_dev_began: 10/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 70SBUR23F00000138
24_hisp_support: Yes
25_hisp_name: U.S. Citizenship and Immigration Services – DHS 
26_public_service: Enable increased self-service through an expansion of digital costumer service tools
31_data_catalog: Other
31_question_type: The applications is utilizing an ML model trained by commercial providers and thus data sets were not required.
33_agency_data: N/A - The Agency does not own any data used to train, fine-tune, or evaluate the use case.
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. 
35_demo_features: Race/Ethnicity,Sex/Gender,Age
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Other
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside 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: Case Processing Improvements in FDNS-DS NexGen
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: In the future, USCIS may consider using artificial intelligence (AI) / machine learning (ML)  FDNS-DS NexGen or other systems  may data from other applications for FDNS-DS NexGen or other systems to aid in investigative work, enhance investigative case prioritization, and detect duplicate case work. USCIS may consider and explore investigating whether it would be useful to integrate AI/ML into the predictive modeling for potential future system enhancements, also integrate AI/ML into the predictive modeling for future system enhancements, working side-by-side with the business stakeholders to develop best practices. Fraud occurs in numerous ways; being able to discover and detect persons with multiple identities allows for more comprehensive investigations, reduces investigative cycle time, and improves performance.
12_outputs: The requirements for this AI use case are currently unspecified, however, future outputs may include recommendations, prioritization/rankings, and anomaly detection pieces to assist with fraud detection and management.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/20/2022
2_use_case_name: USCIS Translation Service
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Document Translation: Integrate AI and GenAI models trained on relevant subject matter (e.g., immigration law, visa/immigration applications, family/adoption certificates, and other sourced processing materials) to provide fast, accurate translation of written or other digital documents in various languages.
Real-time Interpretation: Utilizing AI and GenAI-powered speech-to-speech, speech-to-text translation tools for efficient communication, consultations, and other interactions within DHS. Across all DHS components the need to support language translation and transcription is crucial for operations and adjudications.
12_outputs: Translated documents and translated languages.
16_dev_stage: Initiated
17_impact_type: Rights-Impacting

18_date_initiated: 1/1/2024
2_use_case_name: Verification Match Model
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Leveraging AI in the USCIS verification matching process of known records across systems is beneficial because it streamlines existing USCIS review by 1) improving associated system accuracy, 2) reducing human-error by automating person-and-record match scoring, and 3) matching at a higher volume than traditional tools or manual processes can capably achieve.
12_outputs: A recommendation and score that indicates person-and-record match probability used by verification systems (E-verify and SAVE) to improve accuracy in initial system response.
16_dev_stage: Initiated
17_impact_type: Rights-Impacting

18_date_initiated: 7/12/2023
2_use_case_name: Sentiment Analysis -FOD Field Offices Complaints and Reviews
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: This system indicates the positive or negative feelings people express in their feedback to the U.S. Citizenship and Immigration Services (USCIS).

Survey results categorized to specify the sentiments as positive negative or neutral tone in an excel dashboard.
12_outputs: A graph which categorizes the data into different sentiments using databricks dashboard to see how the customer service can be improved. Does not give any recommendation or decision.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/29/2024