GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Voice Analytics for Investigative Data
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: While conducting investigations, Homeland Security Investigations (HSI) encounters a significant volume of live and recorded content, requiring human language to be extracted and analyzed. Currently, this is achieved through manual processes that are extremely labor-intensive and expensive. To address this challenge, HSI has developed Voice Analytics, a cutting-edge technology that leverages machine learning and advanced speech processing techniques to perform multilingual speech transcription and translation on user-supplied audio files. This innovative solution enables HSI personnel to quickly identify new investigative leads and analyze the outputs against other law enforcement datasets, significantly improving the efficiency and effectiveness of their investigations compared to manual voice analytics processes.
12_outputs: This system outputs transcripted and translated audio files.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 3/1/2020
19_date_acq_dev_began: 7/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: 70OCTD22FR00000002, 70OCTD22FR00000235, 70OCTD22FR00000009
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: 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 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.
35_demo_features: Sex/Gender
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: Repository for Analytics in a Virtualized Environment
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: Semantic Search and Summarization for Investigative Reports
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Effective investigations require querying large volumes of unstructured reports to extract and summarize contextually-relevant information. Current lexical search methods are insufficient for finding such information. Semantic Search and Summarization enables contextually-relevant information extraction and summarization from large volumes of unstructured investigative reports. The output will enhance and optimize investigative analysis by accelerating person-of-interest identification, surfacing trends, and improving network or fraud detection.
12_outputs: The output consists of contextually relevant contents of reports that can be used in investigations to accelerate person-of-interest identification, surface trends, and improve network or fraud detection.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 10/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 70OCTD22FR00000002; 70OCTD22FR00000235; 70OCTD22FR00000009
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: HSI Report of Investigation (ROI) data. HSI ROIs document the findings and results of an investigation into a specific case or matter. ROIs typically include information about the investigation, such as individuals or organizations suspected of violating federal laws related to customs, immigration, or national security. ROIs may also include recommendations for further action, such as prosecution, deportation, or other enforcement measures.
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: 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: 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: Virtual Agent Chatbot for Human Capital
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: The intended purpose of the ServiceNow Virtual Agent natural language understanding (NLU) is to facilitate seamless interactions between users and the chatbot. It aims to provide automated support for common Human Capital related questions and inquiries, enhancing efficiency and user experience.  By understanding natural language, the virtual agent can provide relevant and accurate responses, making it easier for users to get the help they need.  Reduce the amount of submitted inquiries that need to be manually processed by OHC. Reduce the amounts of clicks it takes for the user to find the information they are looking for.
12_outputs: The natural language understanding (NLU) will point towards pre-defined Human Capital related categories that are most relevant based on the users’ inquiries. Specific responses/answers to user questions and resources associated with the questions if applicable.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 5/9/2024
19_date_acq_dev_began: 8/5/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 70CTD023FR0000134
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: ICE Office of Human Capital team members compiled documentation to train the NLU model based on their subject matter expertise on human capital related questions and inquiries.  The data was compiled from knowledge base articles and frequently asked questions for human capital related inquiries.
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: ServiceNow
42_dev_tools_wait: Less than 6 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: Machine Learning Translation Technology Initiative
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The solution will be used to facilitate non-critical communications only. When deployed to production, the solution should not be used alone, absent human quality control, when materials are vital to an individual’s rights or benefits, or when the source materials contain non-literal language (e.g., slang, metaphor), lack clear grammar or structure, contain abbreviations or acronyms, or are overly complex, technical, or wordy. If any interactions indicate the presence of legal or medical issues or a need for greater accuracy, ERO personnel are required to seek additional language assistance via bilingual staff or a professional language line. The solution was first spearheaded by U.S. Coast Guard for maritime usage with the support of DHS Science and Technology Directorate through the Silicon Valley Innovation Program (SVIP). ICE ERO received FY22 funding to initiate the proof of concept with the vendors vetted by USCG and DHS S&T through an interagency agreement. Solutions, in the form of mobile phone applications, are being developed under research and development phases and projected to completely meet ERO’s requirements by January 2026.
12_outputs: The Machine Learning Translation Technology Initiative will offer real-time communication/translation services to assist personnel in providing meaningful access to programs and services for limited English proficient (LEP) noncitizens. AI will be used to provide voice to text, text to voice, and voice to voice translations in at least 21 languages.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/1/2022
19_date_acq_dev_began: 7/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: Mylanguage Phase 3 - 70RSAT23T00000030; Kynamics Phase 4 - 70RSAT24T00000029
24_hisp_support: 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.
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
42_dev_tools_wait: More than 12 months
43_infra_provisioned: No
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: Policy Analyst Assistant
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The Student and Exchange Visitor Program (SEVP) uses AI to help policy analysts quickly find and summarize information about visa rules for students and schools. This saves time and helps analysts respond faster to questions.

This project is intended purpose is to enable SEVP Policy staff to provides responses to policy related inquiries and conduct policy research and analysis: It will be a tool embedded in workflow processes. The processes augmented by the AI includes responses for questions asked by SEVP internal staff and responses given to schools and students by SEVP personnel. For this process, the source of information for the responses will be from authoritative, pre-vetted documents. This reduces the workload on the Policy staff and allows them to focus on more complex policy and guidance issue. For these processes, the AI will assist with researching internal documents and information from regulations, applicable legislation, and other federal guidance. It will assist the policy analyst with summaries, citations, and initial drafts. The benefits includes faster, more consistent responses from Policy and the ability to handle additional work without adding staff.
12_outputs: The outputs provides answers to frequently asked policy and guidance questions and summaries and drafts for more in-depth analysis of policy issues. All responses go to an SEVP staff member and are not the final work product.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/1/2024
19_date_acq_dev_began: 10/14/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 70CTD020FR0000006
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: We are not training the AI, but we are augmenting the AI using RAG (Retrieval Augmented Generation) dataset with SEVP policy documents. These will be used to generate responses and the data will also be to evaluate the answers given.
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: Yes
41_system_name: SEVPAMS
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
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: 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: SEVP Response Center Chatbot - SID (SEVIS Interactive Dialog)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: AI provides SID the ability to understand voices and its deterministic question and answer workflow (1) enables SID to answer routine caller questions without a help desk agent, and (2) when a help desk agent is required, SID will create a ticket with a caller transcript to reduce the burden on the agent. This frees up the human agents to deal with more complex cases and issues with specific records.
12_outputs: SID answers frequently asked questions from callers. If the SID cannot answer a caller’s question, it turns the caller over to an agent in the response center. The chatbot captures the interaction with the caller and sends the information via an API to Student and Exchange Visitor Program Automated Management System (SEVPAMS).
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2/6/2023
19_date_acq_dev_began: 3/6/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: PIID 70CTD020FR0000006/70CTD022FR0000005
24_hisp_support: No
31_data_catalog: 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 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. 
40_has_ato: Yes
41_system_name: SEVPAMS
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: No
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: Title III Semantic Search and Summarization for Translated Content
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The Title III Semantic Search and Summarization functionality will augment translation and transcription services by extracting relevant data using machine learning and natural language processing for correlation and semantic search. Results can then be summarized using a large language model, giving users a tool to target relevant data only. This capability accelerates investigative analysis by rapidly identifying persons of interest, surfacing trends, and detecting networks or fraud, saving hundreds of man hours in manual analysis. HSI will use this tool to generate leads, and further action will be required by investigators and analysts as part of the full investigative process before any action is taken against an individual.
12_outputs: This will allow for the summarization of large data sets using a large language model, giving users a tool to target relevant data only.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 4/12/2022
2_use_case_name: Digital Records Manager (DRM) User Assistance Chatbot
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: This is an AI records search tool to help investigators more efficiently search and gather information. 

The Digital Records Manager (DRM) User Assistance Chatbot is intended to increase user efficiency by providing answers to commonly asked question without the need to manually refer to documentation or submit a help desk ticket.  A reduction in the volume of submitted help desk tickets is expected as a result.
12_outputs: The outputs of the DRM User Assistance Chatbot will be natural language responses to user questions, based on a custom Knowledge Base of DRM documentation artifacts, supported by the natural language capabilities of the backing LLM.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/10/2024
2_use_case_name: Burlington Finance Center Voice Bot
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Identify and verify the caller, retrieve the status of the bond, and share the bond status with the requester and/or answer administrative FAQs.
12_outputs: It will use Natural Language Understanding (NLU)/Natural Language Processing (NLP) to perform voice-to-text and text-to-voice translations, giving it the ability to recognize voices and meaning.  The BFC Voice Bot will be deterministic and will not use Generative AI.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/3/2024
2_use_case_name: RAVEn Compliance Automation Tool (CAT)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
10_commercial_ai: None of the above.
11_purpose_benefits: Repository for Analytics in a Virtualized Environment (RAVEn) Compliance Automation Tool (CAT) is being developed as part of an effort to modernize HSI’s Form I-9 Inspection Process. The goal is to uses machine learning (ML) and automation to increase the speed and efficiency of ingesting and processing Forms I-9 data. Easy to use front-end interface workflow that increases work productivity and reduces manual entry. RAVEn CAT currently employs an Optical Recognition Service (OCR) model and software (Tesseract OCR) to identify pixel coordinates of handwritten and read/extract computer typed characters from ingested forms for processing. Additional research into opensource ML Object Detection models is being made to help further augment accuracy of text identification and extraction of ingested forms into the pipeline. 
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: 7/1/2022
2_use_case_name: Data Tagging and Classification
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
10_commercial_ai: None of the above.
11_purpose_benefits: The Homeland Security Investigations (HSI) Innovation Lab is developing an analytical platform called the Repository for Analytics in a Virtualized Environment (RAVEn). RAVEn facilitates large, complex analytical projects to support ICE’s mission to enforce and investigate violations of U.S. criminal, civil, and administrative laws. RAVEn also enables users to develop new tools to analyze trends and isolate criminal patterns as HSI mission needs arise. For more information, please read the DHS/ICE/PIA-055 - Privacy Impact Assessment 055 for the Repository for Analytics in a Virtualized Environment (RAVEn).  RAVEn leverages data tagging and classification to do the following: The Email Analytics Tool streamlines how special agents and criminal analysts search, filter, translate, and report on electronic communications evidence and will help investigators more effectively determine the structure and organization of criminal enterprises. The RAVEn - Lead Tracker is a centralized system where agents can send and receive leads and enter outcomes such as arrests and seizures. The goal is for all leads in the agency to be found in one place, rather than in various email inboxes. The overarching goal of Mobile Device Analytics is to improve the efficiency of agents and analysts in identifying pertinent evidence, relationships, and criminal networks from data extracted from mobile devices.
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: N/A - Deconsolidation of several use cases
2_use_case_name: Facial Recognition Service
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
10_commercial_ai: None of the above.
11_purpose_benefits: The Facial Recognition Service is used during investigations conducted by Homeland Security Investigations (HSI) agents and analysts for identification of known individuals, as well as extracting faces for further investigations from perpetrators including child exploitation offenses, human rights atrocities, and war criminals.  This is a DHS HSI Innovation Lab/RAVEn project. The Repository for Analytics in a Virtualized Environment (RAVEn) facilitates large, complex analytical projects to support ICE’s mission to enforce and investigate violations of U.S. criminal, civil, and administrative laws. RAVEn also enables tools used to analyze trends and isolate criminal patterns as HSI mission needs arise.
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
18_date_initiated: 8/1/2020
19_date_acq_dev_began: 12/1/2020
20_date_implemented: 8/1/2021
21_date_retired: N/A - Deconsolidation of several use cases