GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Climate Change Assessment Tool
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: Our Nationis already experiencing the adverse impacts from climate change, from sea-level rise, extreme heat, flooding, and drought, to changes in migration patterns and harmful effects on workforce health. DHS needs to be able to better understand and predict where extreme weather conditions will affect DHS assets, including our facilities and our workforce, in the near and long-term. In support of the DHS Climate Action Plan, this AI pilot will enable the detection and streamliinng the assessment of cost/benefit data driven decisions like the location of facilities, prioritization of O&M projects and the health and safety of the DHS workforce. 

Benefits include cost savings, time savings and improved access to critical data and ability to make decisions, improved health and safety of the workforce.
12_outputs: Outputs include the prediction and early detection of extreme weather risks and an integrated understanding of the affects this will have on critical DHS resources. A secondary output is cost-benefit recommendations.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/1/2024
19_date_acq_dev_began: 11/19/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 70RDAD23FR0000148
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: DHS Real Property/Facility Data, DHS Employee and Contractor Data, DHS Facility utilization (e.g. energy usage) Data
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: Management Cube (MGMT Cube)
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
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: Spending Analysis and Budget Execution Risk (SABER) Model
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: Predicts potential budget execution issues by analyzing historical spending patterns across various Treasury accounts and classifications.
Benefits: Early identification of spending anomalies allows proactive budget management and reduces the risk of under/overspending.
12_outputs: Warnings, flags for review and comparison via prediction and classification.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/22/2024
19_date_acq_dev_began: 2/19/2024
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: Treasury data with slight report-centric fields added. The data is publicly available.
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: AdaptiveMFA
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: Adaptive Multi-factor Authentication (AMFA) introduces additional intelligence into Identity flows by taking into account the authentication context data during the authentication. Using the data, DHS is able to adapt security and authentication policies to enhance the security to DHS systems.
12_outputs: The input includes: device, network, location, travel, IP, and external data from third parties and endpoint security integrations. The output are Risk ratings HIGH, MEDIUM, LOW for each authentication attempt which can be configigured to require stricted access comtrol policies.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/1/2024
19_date_acq_dev_began: 3/30/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 47QFRA24F0005
24_hisp_support: No
31_data_catalog: No
33_agency_data: The system collects signal data during authentication to dynamically build behaviors of the user.
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: The DHS Next Generation Authentication as a Service (DHSAuthPortal) System
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
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: ESEC Inquiry (STORM) Summarization
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: ESEC-STORM AI employs Generative Artificial Intelligence (Gen-AI) technology to automate the creation of document summaries. This advanced system facilitates the automatic integration of these summaries into the System of Tracking, Operations, and Record Management (STORM), thereby optimizing the management of correspondence and information requests. 
12_outputs: When a user creates a new work package and uploads a letter, it activates a Power Apps workflow that creates a summary.
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
20_date_implemented: 12/13/2024
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: 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 widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
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: Dynamics Online As a Service; BIaaS
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: 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: One Health Threat Detection and Risk Assessment Platform (OH-TREADS) / Planner
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: OHS
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Visuals will be created using a broad variety of data pulled from open-source, public, and non-public sources. AI/ML will make it possible to ingest data from a variety of digital formats, and translate it to usuable information into machine-readable data. The volume of data, and lack of clear data collection standards, requires AI to help merge streams with different data ontologies while facilitating data interoperability with mis-paired data sets. Neither activity is, or can be, practically performed by people. AI/ML will be further used in Planner to generate risk and predictive scores in addtion to displaying relevant information and analyses that help analysts understand health security threats and broadly monitor the health security landscape. AI/ML is meant to provide comprehensive situational awareness for health surveillance and public health response, with target capabilities that: enable global situational awareness of current and potential health risks from a One Health perspective; assist in early warning of health threats by location, facility, and species; aid in rapid identification of health threats at population, facility, and greater geographic resolution; and support data-driven decision making to prevent, mitigate, and respond to health threats.
12_outputs: The main AI system outputs will be anomaly detection following the translation of information into a structured, machine-readable datasets. AI/ML is then used for risk identification and disease prediction that are overlayed on a map and displayed with other wholistic visuals. The visuals and analytics can then be combined with critical infrastructure locations, resources, or capabilities (feeral, state, local, tribal, and territorial) to aid response and decision making.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2023
2_use_case_name: Sentiment Analysis and Topic Modeling (SenTop)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: MGMT
10_commercial_ai: None of the above.
11_purpose_benefits: The initial purpose of the Sentiment Analysis and Topic Modeling (SenTop) project was to analyze survey responses for DHS’s Office of the Chief Procurement Officer related to contracting. However, it has evolved to be a general-purpose text analytics solution that can be applied to any domain or area. It has also been tested and used for HR topics. SenTop is a DHS-developed Python package for performing descriptive text analytics, specifically, sentiment analysis and topic modeling on free-form, unstructured text. SenTop uses several methods for analyzing text, including combining sentiment analyses and topic modeling into a single capability, permitting identification of sentiments per topic and topics per sentiment. Other innovations include the use of polarity and emotion detection, fully automated topic modeling, and multi-model/multi-configuration analyses for automatic model/configuration selection. The code has been established, performs an analysis, and provides a report, but it is only accessed and run by one person per customer request.
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
22_dev_method: Developed in-house.
24_hisp_support: No
2_use_case_name: DHS-Chat
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: This is a chatbot based on a Large Language Models (LLM) for internal DHS employee use - It's like chat-GPT for DHS but approved for use with non-classified but internal information (this includes FOUO (For Official Use Only) CUI (Controlled Unclassified Information) due to its improved security compared to publicly available chatbots. 

This tool is able to dynamically create written content through text prompts submitted by the user. Approved applications of this tool to DHS business include generating first drafts of documents that a human would subsequently review, conducting and synthesizing research on open-source information and internal documents, and developing briefing materials or preparing for meetings and events.
12_outputs: The internally available generative AI tool outputs text based on the users input.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/1/2024
20_date_implemented: 12/12/2024
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 widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
41_system_name: (Pending)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Other
45_question_type: Compute resourcing increases are managed through the centralized IT infrastructure and working with Microsoft Azure.
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 and Entity Behavior Analytics (UEBA)
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: Enhanced Threat Detection: Identify patterns in user behaviors that deviate from normal baselines, signaling potential insider threats or security risks. Continuous Risk Assessment: Move from static vetting to a continuous vetting process by monitoring real-time activities and interactions with secure information.
Improved Incident Response: Enable rapid responses to high-risk behaviors, escalating alerts for timely intervention by security personnel."

12_outputs: Behavioral Baseline Modeling: Develop a baseline for each individual based on regular access patterns, network usage, and interactions with classified data or secure areas.
Anomaly Detection: Employ machine learning models to detect deviations from established baselines, such as unusual access times, atypical access to high-sensitivity resources, or excessive data downloads.
Risk Scoring: Assign a risk score to each user based on observed anomalies, factoring in historical behavior, job role, and access level, allowing security teams to prioritize investigations.
Automated Alerts & Reporting: Generate automated alerts for high-risk behaviors or patterns of concern and deliver timely reports to personnel security teams for further investigation."

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2020
19_date_acq_dev_began: 4/3/2021
20_date_implemented: 7/8/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: PIID Pending, not yet issued.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: PII, SPII , Clearance and Background Investigation Data: Data from security clearances, background investigations, and adjudication records.
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: Vetting Identities for an Enterprise Workforce
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: 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: Text Analytics for Survey Responses (TASR)
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: The intended purpose of the AI is to perform topic modeling,  sentiment analysis, or other text classification tasks on responses provided to internal staff DHS Pulse Survey questions.

Text Analytics for Survey Responses (TASR) is an application for performing Natural Language Processing (NLP) and text analytics on survey responses. It is currently being applied by DHS Office of the Chief Human Capital Officer (OCHCO) to analyze and extract significant topics/themes from unstructured text responses to open-ended questions in the quarterly DHS Pulse Surveys. Results of extracted topics/themes are provided to DHS Leadership to better inform agency-wide efforts to meet employees’ basic needs and improve job satisfaction
12_outputs: The systems outputs include a set of topics inferred or surfaced from the raw text comment data, as well as sentiments or other classifications inferred from the data.
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: 11/1/2022
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 widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
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: 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: MiX Phenotyping
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: OHS
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Electronic medical records can provide key data for health security insights. AI/ML makes it possible to translate these records into machine-readable data, which is the first step to finding these health security insights. AI/ML will then be used to find clinical patterns across the data, such as patterns in symptoms over time and location. AI/ML will also be used to automate the process for detecting new trends (anomalies) in these clinical patterns. These health security insights can alert about potential threats, inform messaging, and provide decision support to medical and public health partners.
12_outputs: The main AI system outputs will include anomaly detection for emerging trends in clinical record patterns. AI/ML outputs will also be used to find these clinical patterns by clustering, classification and topic modeling. These clinical patterns will finally be output as linear reference models that are simple enough to be interpreted and guided by human clinicians using human-in-the-loop collaboration.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2021
2_use_case_name: MiX Indicators
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: OHS
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Online news and other open-source media can be used to quickly detect and respond to emerging health security threats. However, it is not practical for people to read and scan all online news stories every day for key terms. AI/ML makes it possible to read, digest and organize news stories to look for key threat terms. AI/ML will also be used to predict normal trends in news stories. This makes it possible to detect when an unusual number of health security threat terms are found in the news. This use of AI/ML helps prepare for, respond to, and protect from potential health security threats.
12_outputs: The main AI system output will be anomaly detection which represents two elements: 1) AI/ML predictions for usual trends in news story key terms vs. 2) the actual daily number of mentions for key terms in the news. When the actual number of mentions for key terms exceeds modeled predictions, these will be detected as anomalies.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2023
2_use_case_name: MiX MedINT (Medical Intelligence Dashboard and Canvas)
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: OHS
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Health security visuals will be created using a broad variety of data pulled from online sources. AI/ML will make it possible to translate this information into machine-readable data. AI/ML will also find relevant terms in these online articles, such as for locations and diseases of interest, label articles accordingly, and rank articles for relevance to topics of interest. These necessary steps cannot practically be performed by people. AI/ML will then support MedINT Canvas to  visualize connections between topics of interest, such as the presence of key symptoms and disease diagnoses in certain locations. Given the wide variety of potential health security threats, wholistic visual tools are needed to allow analysts to broadly monitor the health security landscape.
12_outputs: The main AI system output will be the translation of online information into a structured, machine-readable dataset. This includes language translation for non-English articles, labeling articles by health threat types, and ranking articles by relevance to health threats. This dataset will then be used to create the wholistic visuals in Canvas and Dashboard.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2021