GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Asylum Text Analytics
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
10_commercial_ai: None of the above.
11_purpose_benefits: USCIS oversees lawful immigration to the United States. As set forth in Section 451(b) of the Homeland Security Act of 2002, Public Law 107-296, Congress charged USCIS with administering the asylum program. USCIS, through its Asylum Division within the Refugee, Asylum & International Operations Directorate (RAIO), administers the affirmative asylum program to provide protection to qualified individuals in the United States who have suffered past persecution or have a well-founded fear of future persecution in their country of origin, as outlined under Section 208 of the Immigration and Nationality Act (INA), 8 U.S.C. § 1158 and Title 8 of the Code of Federal Regulations (C.F.R.), Part 208. Generally, an individual not in removal proceedings may apply for asylum through the affirmative asylum process regardless of how the individual arrived in the United States or his or her current immigration status by filing Form I-589, Application for Asylum and for Withholding of Removal. The ATA capability employs machine learning and data graphing techniques to identify plagiarism-based fraud in applications for asylum status and for the withholding of removal by scanning the digitized narrative sections of the associated forms and looking for common language patterns. 
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: 1/25/2019
19_date_acq_dev_began: 1/25/2019
20_date_implemented: 10/1/2019
21_date_retired: 10/1/2024
2_use_case_name: Biometrics Enrollment Tool (BET) Fingerprint Quality Score
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
10_commercial_ai: None of the above.
11_purpose_benefits: Duplicate with BET Maximization. The Biometrics Enrollment Tool (BET) team has incorporated the National Institute of Standards and Technology (NIST) Fingerprint Image Quality 2 (NFIQ2) algorithm (a trained machine learning algorithm) for scoring fingerprints (https://www.nist.gov/services-resources/software/nfiq-2) into the BET application. This algorithm takes a fingerprint image and assigns a score between 0 and 100, with 100 indicating the best quality fingerprint image that could be obtained. The higher the score, the more likely it is that the fingerprint will match when captured again.
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 -Consolidated into another use case.
2_use_case_name: Timeseries Analysis and Forecasting
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
10_commercial_ai: None of the above.
11_purpose_benefits: USCIS is the component within DHS that oversees lawful immigration to the United States. That means USCIS receives, processes, and maintains all applications for admission for Lawful permanent residents (LPRs), or adjustments to LPR status. Also known as “green card” holders, LPRs are non-citizens who are lawfully authorized to live permanently within the United States and are required to fill out Form I-90, Application to Replace Permanent Resident Card (Green Card). Since there has been a considerable influx of green card applications, USCIS used a combination of exploratory data analysis to determine the most used categories for applicants submitting I-90's and machine learning to create predictions of workloads. As a follow-on, USCIS used Autoregressive Integrated Moving Average (ARIMA) models on the I-90 form, which allowed the prediction of the total number of forms for a 2-year period. ARIMA is one of the easiest and effective machine learning algorithms to perform time series forecasting. This capability has been deployed in production for more than a year. This model was eventually enhanced using ML model to have better reusability and performance.
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: 1/1/2020
19_date_acq_dev_began: 1/1/2020
21_date_retired: 6/1/2022
2_use_case_name: Testing Performance of ML Model using H2O
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
10_commercial_ai: None of the above.
11_purpose_benefits: USCIS is the component within DHS that oversees lawful immigration to the United States. That means USCIS receives, processes, and maintains all applications for admission for Lawful permanent residents (LPRs), or adjustments to LPR status. Also known as “green card” holders, LPRs are non-citizens who are lawfully authorized to live permanently within the United States and are required to fill out Form I-90, Application to Replace Permanent Resident Card (Green Card). Since there has been a considerable influx of green card applications, USCIS used a combination of exploratory data analysis to determine the most used categories for applicants submitting I-90's, and machine learning to create predictions of workloads. USCIS used the H20 machine learning model to allow USCIS analysts to build and run several machine learning models on big data in an enterprise environment and identify the model that performs the best. It has already been successful in identifying the most accurate model for the I-90 Form Timeseries Analysis and Forecasting use case. 
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: USCIS  Consular Consolidated Database (CCD) Facial Recognition (FR) On Demand Report (VISA Only)
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: USCIS uses the Facial Recognition (FR) on Demand report (Visa only) to combat fraud by benefit applicants whose fingerprints are not in IDENT but who may have photos in the Department of State's (DoS) Consular Consolidated Database (CCD) that predate the fingerprinting of visa applicants. 
12_outputs: Match or no match response.
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

20_date_implemented: 4/1/2019
54_key_risks: CCD is operated by the DoS Bureau of Consular Affairs. DHS works closely with DoS on risks and mitigations.
2_use_case_name: Sentiment Analysis - Employee Satisfaction Surveys
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
10_commercial_ai: None of the above.
11_purpose_benefits: Survey results cosed to specify the sentiments as positive negative or neutral tone in a excel dashboard
12_outputs: A graph which categorizes the data into different sentiments using datanricks dashboard  how the customer service can be improved. Does not give any reccomendation or decision.
16_dev_stage: Retired
18_date_initiated: 1/1/2019
20_date_implemented: 1/1/2019
21_date_retired: 10/1/2024
2_use_case_name: Sentiment Analysis - ELIS Case Notes
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
10_commercial_ai: None of the above.
11_purpose_benefits: Survey results cosed to specify the sentiments as positive negative or neutral tone in a excel dashboard
12_outputs: A graph which categorizes the data into different sentiments using datanricks dashboard  how the customer service can be improved. Does not give any recommendation or decision.
16_dev_stage: Retired
17_impact_type: Rights-Impacting

21_date_retired: 10/1/2024
2_use_case_name: Predicted to Natzuralize
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
10_commercial_ai: None of the above.
11_purpose_benefits: The Predicted to Naturalize model predicts when Legal Permanent Residents would be eligible to naturalize and attempts to provide a current address. This model could potentially be used to send correspondence to USCIS customers of their resident status and notify others of potential USCIS benefits.
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: 1/1/2023
19_date_acq_dev_began: 1/1/2023
21_date_retired: N/A - This use case was never initiated and never formally retired.
2_use_case_name: I-485 Family Matching
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
10_commercial_ai: None of the above.
11_purpose_benefits: I-485 Family Matching is designed to create models to match family members to underlying I-485 petitions. The underlying immigrant petition defines if the I-485 is employment-based or family-based. It also has information about the visa classification and priority date which, when compared against the Department of State’s monthly Visa Bulletin, helps predict visa usage. It is difficult to match an I-485 to its underlying immigrant petition, because the only available field on which to match is the Alien-number (A-number). This number is not always present on the immigrant petition, and name/date of birth matching is not as reliable.  The goal of I-485 Family Matching is to leverage AI to create connections more confidently between petitioners and their families based on limited data.  Additionally, it was intended to help identify and group I485s filed by family members, as well as gather up the many ancillary forms they may have been pending (such as I765, I131). Similar to immigrant petition matching, it can be difficult to match up I485s filed by family members. In these cases, the only similar fields are a common address. Efforts have been made in the past to identify family members by address, but it is effective only to a point. This use case explored the technical feasibility of using an AI model to help make working with this data more reliable (as well as group individual petitioners, their families, and other helpful associated data together for faster and more accurate processing).  Work on this use case did not go beyond ideation and no AI model was built (and no output was rendered) after the proof-of-concept established that the desired end-state was infeasible with the technology available at that time.
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: 1/1/2023
19_date_acq_dev_began: 1/1/2023
21_date_retired: N/A - This use case was never initiated and never formally retired.
2_use_case_name: Topic Modeling on Request For Evidence (RFE) Data Sets
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
10_commercial_ai: None of the above.
11_purpose_benefits: Builds models that identify lists of topics and documents that are related to each topic. Topic modeling provides methods for automatically organizing, understanding, searching, and summarizing text data. It can help with the following: discovering hidden themes in the collection. classifying the documents into the discovered themes. 
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: 1/1/2021
19_date_acq_dev_began: 1/1/2021
21_date_retired: 10/1/2024
2_use_case_name: I-539 approval prediction
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
10_commercial_ai: None of the above.
11_purpose_benefits: Builds models that identify lists of topics and documents that are related to each topic. Topic modeling provides methods for automatically organizing, understanding, searching, and summarizing text data. It can help with the following: discovering hidden themes in the collection. classifying the documents into the discovered themes.
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: 1/1/2021
19_date_acq_dev_began: 1/1/2021
21_date_retired: 6/1/2021
2_use_case_name: Biometrics Enrollment Tool (BET) Fingerprint Maximization
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: BET assists in determining if the fingerprint taken is good enough quality to pass the FBI fingerprint check process. It provides immediate feedback when a set of prints is likely to be rejected by the FBI by incorporating machine learning models into the BET application. The FBI will not disclose their quality grading criteria for fingerprints, leaving BET with the responsibility of determining quality to prevent unnecessary secondary encounters with applicants.
12_outputs: Numerical Fingerprint Quality score, which is compared against  fingerprint quality thresholds (per finger and per set of fingerprints) to align with FBI specifications
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2021
19_date_acq_dev_began: 5/23/2023
20_date_implemented: 1/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 70SBUR23F00000126
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Internal data from BET data capture into Databricks lakehouse, numerical values representing fingerprint quality scores determined by the BET system outside of the AI workflow.
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: Biometrics Enrollment Tool
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: ELIS Evidence Classifier Machine Learning (ML) Tagging Solution
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: To enable end users to navigate directly to the page(s) containing evidence documents of interest instead of sifting through large PDF documents. Evidence tagging intends to accelerate case processing by identifying specific types of documents (e.g., I-589, passport photo spread, marriage certificate) and applying a metadata tag to that document object in ELIS. This way, when a user opens a case with potentially hundreds of pages of evidence documents, rather than scrolling through them one at a time to find a specific document of interest, they have clickable "bookmarks" in the UI generated from these tags that will jump directly to the corresponding page.
12_outputs: Tagged evidence. The system inputs an image (scanned document from Lockbox) and outputs either a specific label, such as "Border Crossing Card - Front," or no label if that document is not recognized as one of the classes.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 11/20/2019
19_date_acq_dev_began: 11/20/2019
20_date_implemented: 9/1/2020
22_dev_method: Developed with contracting resources.
23_contract_piids: 70SBUR23F00000120
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Applicant submitted scanned evidence documents sent to ELIS and Global for processing.
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: ELIS
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: Automated Name and Date of Birth (DOB) Harvesting from Existing Records
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: To reduce the amount of adjudicative time spent manually harvesting aliases and dates of birth (DOBs) from identity history summary (idHS) report attached to the ELIS case as part of the Manual Name Harvesting Task during case processing.
12_outputs: Eliminates need for manual review by extracting unique names and DOBs from IdHS documents and when names/DOBs already in ELIS, ANH will not suggest any names.This is still a human in the loop process. The ELIS user performing MNH tasks prompted to make a decision if the suggested names and DOBs are related to case hence are able to accept/reject the suggestions.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/10/2020
19_date_acq_dev_began: 7/10/2020
20_date_implemented: 6/27/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: 70SBUR23F00000120
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: 1. To train this deep learning model, we’ve gathered the text from about 8,000 IdHS records.
2. Typically, annotation of these entities would be done by hand. But in this case, we’re able to leverage the manual name harvesting that’s already been completed by ELIS users and therefore systematically tag our training data.
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: ELIS
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: ELIS Card Photo Validation via myUSCIS
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: USCIS uses a system called ELIS to manage immigration requests, and it includes a Photo Validation Service to check if ID photos meet requirements. This helps ensure photos are correct before making ID cards, saving time and avoiding delays.

The photo validation service uses a combination of computer vision techniques and machine learning models to validate photographs and ensure they meet the requirements, so these photos can be used in card production.
12_outputs: Response back to user based on the pre-defined quality checks if the uploaded photo meets USCIS requirements. Users still have the option to ignore the warnings and upload the photo.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 12/9/2020
19_date_acq_dev_began: 12/9/2020
20_date_implemented: 3/15/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: 70SBUR23F00000120
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Applicant submitted scanned or digital photographs sent to ELIS for processing, used for creating custom models or fine-tuning open source models and evaluation.
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: ELIS
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.