GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Machine Translation
(Previously Language Translator)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: ICE
10_commercial_ai: None of the above.
11_purpose_benefits: The Rapid Analysis and Visualization Engine for Reporting (RAVEn) platform provides capabilities to upload data that may contain text in languages other than English. To support the agent/analyst analysis of the ingested data as well as to provide for the means to standardize searches across RAVEn data sources, RAVEn provides language translation capabilities to create text conversions between the original language and English. Data translation can be performed on ingest or on an ad hoc basis. Solution will provide translation support of, at minimum, plain text, word documents, and portable document formats using Natural Language Processing and machine learning.
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: 2/1/2021
19_date_acq_dev_began: 8/1/2019
20_date_implemented: 8/1/2022
21_date_retired: N/A -Consolidated into another use case.
2_use_case_name: Investigative Prioritization Aggregator
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 sheer volume of data associated with investigations often overwhelms human capabilities, making it challenging for HSI personnel to analyze evidence and identify key players in criminal networks. Currently, there is no effective mechanism to quantify the level of evidence related to a particular subject or entity, or to determine which actors within a network are the most influential. This is particularly critical in the context of the counter-opioid/fentanyl mission, where timely and accurate intelligence is essential.

To address this challenge, this project utilizes machine learning to assign point values to data, enabling the scoring of information associated with a given selector, such as a phone number or legal name. This scoring system helps to understand the importance of an entity to investigations and the potential consequences of removing or neutralizing that entity. By doing so, HSI personnel can focus on high-priority targets and associated criminal networks, ultimately enhancing their ability to disrupt and dismantle these threats.
12_outputs: The output is scored entity data (such as a phone number or legal name). This scoring system helps to understand the importance of an entity to investigations and the potential consequences of removing or neutralizing that entity.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/1/2021
19_date_acq_dev_began: 10/1/2022
20_date_implemented: 2/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 196918HSI00013
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Law Enforcement Sensitive (LES) investigative data.
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: 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: AI Assisted Compromise Email Detector (AACED)
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: The use case was developed to assist ICE SOC in reviewing a collection of emails between ICE personnel and Microsoft that were part of ED 24-02.  The use case provides a faster mechanism to the SOC analysts to determine indicators of compromise, reducing the level of effort for these individuals’ analysis exponentially.  To assist the analysts, Named Entity Recognition (NER) was used to detect PII and other associated keywords to increase analyst productivity, and reduce time required to analyze emails.

12_outputs: Outputs are named entities and generated text for specific questions. Chat interface for analyst to conduct Q&A with email as context.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2024
19_date_acq_dev_began: 5/1/2024
20_date_implemented: 6/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Stored Agency emails used for validation.
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: 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 for Workflow Automation
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: Business units within ICE leverage these services to automate repeatable, time-consuming processes such as invoice processing, and form entry validation and extraction. This platform will provide Optical Character Recognition and machine learning models to verify, extract, and classify information from ICE forms. Using AI to provide information extraction for these processes saves ICE personnel a significant amount of time while improving data quality and enabling automation.

12_outputs: This platform will provide Optical Character Recognition and machine learning models to verify, extract, and classify information from ICE forms.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2019
19_date_acq_dev_began: 1/1/2019
20_date_implemented: 6/1/2019
22_dev_method: Developed with contracting resources.
23_contract_piids: 70CTD021FR0000248; 70CDCR24FR0000048; 70CMSD21FR0000169; 70CTD021FR0000248; 70CTD023FR0000003; 70CTD020FR0000006; 70CTD022FR0000005
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: ICE’s document understanding platforms provide out-of-the box models to verify and/or extract information from a variety of document types. These platforms also include the ability to create an ML feedback loop to tailor the models to try to improve the accuracy in extracting data fields from multiple document types. In this scenario, ICE would fine-tune the document understanding model using the documents submitted to the understanding workflow.
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: UiPath Suite, Azure AI Document Intelligence
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: 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: Cybersecurity Threat Management, Detection, and Response
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: Machine Learning (ML) is used to analyze historical ICE cybersecurity data, create activity baselines, and detect anomalous behaviors for alerting. By integrating ML with other advanced analytics and correlation rules, AI identifies patterns indicative of security incidents, enabling SOC analysts to detect and respond to threats more quickly than previously possible.
12_outputs: The output of the analytics will be anomaly detection from patterns in security tool data correlated into alerts for SOC analysts.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/21/2020
19_date_acq_dev_began: 9/17/2020
20_date_implemented: 3/16/2021
22_dev_method: Developed in-house.
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: Yes
41_system_name: Xploitation Platform Leveraging Offensive Information Technology, Phishing Defense Solution, Audit Log Management Solution (ALMS), Endpoint Detection and Response, EMVEC
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
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: Translation and Transcription 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: HSI investigators often encounter data from various sources, including legal and administrative processes, enforcement actions, and open-source materials, in languages other than English. To unlock the value of this data, it must be translated into English before further analysis can be conducted. The Translation and Transcription Service leverages neural machine translation (NMT) models for text translation and automatic speech recognition (ASR) and deep neural network (DNN) models with normalization for voice-to-text transcription. This innovative approach enables users to quickly triage large datasets and identify key information relevant to investigations. Any data deemed critical for court proceedings is then submitted to certified human translators for final review, ensuring that government resources are allocated efficiently and only used for necessary translations and transcriptions.

12_outputs: The Translation and Transcription Service leverages neural machine translation (NMT) models for text translation and automatic speech recognition (ASR) and deep neural network (DNN) models with normalization for voice-to-text transcription.

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2022
19_date_acq_dev_began: 10/1/2022
20_date_implemented: 2/1/2024
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: AI models within the use case are not trained on agency data. Open-source models (i) Whisper is trained on 680K hours of multilingual and multitask supervised data collected from the web, (ii) No Language Left Behind (NLLB) is trained on a combination of publicly available datasets (additional information available in Section 5 of Meta’s NLLB whitepaper: https://research.facebook.com/file/585831413174038/No-Language-Left-Behind--Scaling-Human-Centered-Machine-Translation.pdf). 
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: RAVEn, Azure AI Translator
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: Email 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: HSI personnel encounter large volumes of legally acquired, multilingual email data that must be prepared (ingested, triaged, translated, searched and filtered) before it can be analyzed to support investigations. The Email Analytics application eliminates manual data preparation processes, and leverages machine learning to conduct spam message classification, translation, and entity extraction, including names, organizations, or locations. It also utilizes HSI's AI-enabled translation capabilities (see related use case “Translation and Transcription for Investigative Data”) for translation of emails in other languages to English. The output reduces time and resources spent preparing data, increases the analytic utility of the data, and allows HSI personnel to more quickly conduct analysis on the information.
12_outputs: The output is email data that has undergone spam message classification, translation, and entity extraction, including names, organizations, or locations.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/1/2019
19_date_acq_dev_began: 8/1/2021
20_date_implemented: 8/1/2021
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: Lawfully obtained email data.
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: 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: Identification Card and Travel Document Code Detection
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 Identification Card and Travel Document Code Detection service leverages mobile phone cameras to quickly and accurately read 2D Data Matrix Codes on US Driver's Licenses and ID cards, as well as Machine Readable Zones (MRZ) on travel documents like Passports and Passport cards. This innovative solution utilizes machine learning to enhance the detection of these codes, allowing for seamless scanning and automatic population of detected information into corresponding text fields within HSI mobile apps. By streamlining the code recognition process, the service significantly reduces the time required for users to read cards or documents, eliminating the need for manual data entry and resulting in more efficient, in-person interactions with individuals.
12_outputs: The output is a dataset of structured identification data.

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2020
19_date_acq_dev_began: 11/1/2020
20_date_implemented: 6/1/2021
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: 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.
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: JES and Appropriations Insight
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: MGMT
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Purpose: Converts scanned financial tables from PDFs into structured, machine-readable data while maintaining multi-year spending relationships.
Benefits: Eliminates manual data entry, reduces errors, and significantly speeds up the process of consolidating historical financial data from legacy documents.
12_outputs: Structured tables in place of free text to provide a machine-readable dataset.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/1/2024
19_date_acq_dev_began: 4/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 70RDAD22FR0000064
24_hisp_support: No
31_data_catalog: Other
31_question_type: OCFO uses Collibra, through CDOD's Matrix environment. The catalog build-out is WIP.
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.
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: CFO Companion
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: MGMT
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Provides authorized staff with an intuitive, conversational interface to query and analyze DHS CFO financial data and reports.
Benefits: Democratizes access to financial information, reduces time spent searching through documents, and enables quick self-service analytics without specialized database knowledge.
12_outputs: On-demand information retrieval via natural language processing.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 4/1/2024
19_date_acq_dev_began: 4/8/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 70RDAD22FR0000064
24_hisp_support: No
31_data_catalog: Other
31_question_type: OCFO uses Collibra, through CDOD's Matrix environment. The catalog build-out is WIP
33_agency_data: Internal system and process information, such as the link to a visualization, or data attribute information tied to publicly available finanacial data.
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.
40_has_ato: Yes
41_system_name: CFO Horizon
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.