GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Mibanco: Equitable Finance Activity in Colombia
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Colombia
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: MICOMUNIDAD Ecosystem: Create a relational ecosystem called Micomunidad, which generates impact for the development and growth of communities, involving different actors that adapt the collaborative work model, promoting financial inclusion, contributing to the reduction of poverty, in a sustainable and easily achievable way, weaving long-term relationships in vulnerable areas. One of the activities is the ChatBot for Financial Education, which is an innovative digital tool that seeks to improve the management of personal and family finances.
12_outputs: Chatbot interface
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/15/1905
19_date_acq_dev_began: 7/15/1905
20_date_implemented: 7/16/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72051422D00003
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: Remote sensing for land cover identificiation in Colombia
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Colombia
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Training dataset with water body identification capacbility using Landsat and Sentinel Imagery as input
12_outputs: Annotated maps
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/16/1905
19_date_acq_dev_began: 7/16/1905
20_date_implemented: 7/16/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72051423D00003 
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: AI to support road construction in rural Colombia
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Colombia
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: An AI-based chatbot that supports the "Caminos Comunitarios para la Paz Total" Program seeks to bring to scale the Program's impact across Colombia. By deploying an AI-driven solution, the implementing partner can enhance its outreach and operational efficiency, enabling communities seeking to improve their road networks to access vital information regarding technical and legal requirements quickly through the chatbot solution.
12_outputs: Chatbot interface
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/16/1905
19_date_acq_dev_began: 7/16/1905
20_date_implemented: 7/16/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72051425P00003
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: Mobile application for reading rapid cryptococcosis tests
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Guatemala
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: To reduce human error of reading of cryptococcosis tests, the app is used to confirm healthcare worker reading of test results
12_outputs: A positive or negative test result of a cryptoccosis test that a healthcare worker uses to confirm their original diagnosis
16_dev_stage: Implementation and Assessment
17_impact_type: Both
18_date_initiated: 11/1/2023
19_date_acq_dev_began: 6/1/2024
20_date_implemented: 12/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 72052023FA00001
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: Test-reading error (false positive or false negative)
56_monitor_postdeploy: Automated and Regularly Scheduled Updates: Some aspects of the plan to monitor the AI system post-deployment are automated including re-training of models after detecting drift; however, data science teams are still significantly involved in the monitoring and re-deployment process.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: In-person
61_adverse_impact: No
62_disparity_mitigation: N/A. Photos of test kits with results are the same for all demographics. Patient nor provider demographics are used in the system
63_stakeholder_consult: Direct user testing
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Chatbot to respond to teachers' questions about how to prevent student dropout
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Guatemala
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: To provide smoother interaction in a chatbot that guides teachers to options for interventions to reduce the likelihood of student dropout
12_outputs: Customized recommendations for teachers based on pre-established protocol of steps to follow to reduce the likelihood of student dropout
16_dev_stage: Acquisition and/or Development
17_impact_type: Rights-Impacting

18_date_initiated: 7/1/2024
19_date_acq_dev_began: 8/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 2052022CA00001
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: No – agency does not have access to source code.
41_system_name: Dropout Early Warning System Assistant
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Benchmark evaluation: Testing of the AI model has involved the use of benchmarks (either publicly available or internally created) to estimate performance in real-world settings, but has not been tested in an operational environment. 
54_key_risks: Minor risk of chatbot providing incorrect answers to teachers' questions. 
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
61_adverse_impact: No
62_disparity_mitigation: No student or teacher infomation interacts with the proposed AI component of the larger app.
63_stakeholder_consult: Direct user testing
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Predictive analytics model to predict HIV Treatment Interruption Risk
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Guatemala
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Predict HIV Treatment Interruption Risk 
12_outputs: Flagging of patients who may be at risk of dropping out of care
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 4/1/2024
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 7200AA19CA00004
24_hisp_support: No
27_public_info: No
29_contains_pii: No
31_data_catalog: Yes
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.
35_demo_features: HIV positive individuals
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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.
51_extension_request: No – Agency did not request an extension for this use case.
61_adverse_impact: No
2_use_case_name: Anti-Money Laundering and Shareholders Transparency IT System
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Moldova
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: The implemented Anti-Money Laundering (AML) system currently relies on a rule-based detection framework with advanced networking capabilities. Building on this foundation, additional machine learning (ML) and artificial intelligence (AI) capabilities are being supported to enhance the accuracy of detection logic. This innovative system, the first of its kind in the region, equips employees of the National Bank of Moldova (NBM) with advanced tools to monitor all banking operations comprehensively. The system supports the NBM’s risk-based approach, enabling the identification of suspicious activities and raising of timely alerts. This supports early detection of money laundering and terrorist financing risks, as well as suspicious changes in the ownership structures of banks. Importantly, the NBM's role is supervisory rather than investigative; the system is not designed for law enforcement or prosecutorial purposes.


As part of the implementation parameter, a ML/AI proof of concept has been developed. This ML/AI use case specifically focuses on enhancing detection ratios by analyzing alert datasets and generating a segmentation scheme through a K-means clustering algorithm. This segmentation helps identify distinct subgroups (e.g., customers, accounts) with varying levels of money laundering risk, thereby refining the detection process and supporting risk-focused oversight.

12_outputs: The primary output of the AI algorithm is a segmentation scheme, as described above. This scheme categorizes entities based on their risk levels, enabling improved detection logic and risk assessment within the AML framework.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/1/2021
19_date_acq_dev_began: 4/1/2021
20_date_implemented: 8/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 72011719F00001
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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.
35_demo_features: Gender, other
35_question_type: Address, residency, affiliation with different groups, etc.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: AI analysis of Moldova's media content and social media landscape
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Moldova
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: This project utilizes AI to provide analysis of Moldova's media landscape, focusing on narrative framing techniques in both Romanian and Russian-language media and social media. The goal of this application is situational awareness and strategic communications planning, not content removal or blocking.
12_outputs: Monthly Reports
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 10/1/2023
20_date_implemented: 6/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: AID-117-A-17-00001
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: No
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: "Bu Mira" financial inclusion chatbot
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Indonesia
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Bu Mira is designed to interface with women entrepreneurs as a business partner on WhatsApp. Through Bu Mira, women entrepreneurs will be able to access educational modules, financial tools, and loan applications.
12_outputs: WhatsApp messages
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/25/2022
19_date_acq_dev_began: 1/25/2022
20_date_implemented: 1/16/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 72049722FA00003
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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.
35_demo_features: Gender
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: Machine translation for communication with country teams
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/El Salvador
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Machine translation eases communication between the in-country team (which operates entirely in Spanish) and English-speaking colleagues.
12_outputs: Written text
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/20/2024
19_date_acq_dev_began: 7/15/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 7200AA19D00015, 7200AA24F00002
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: No – agency does not have access to source code.
40_has_ato: No
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: Crop2Cash National Hotline for Agriculture
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Nigeria
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: This  platform is an AI-powered advisory system accessible via phone call. It is available all the time (24/7) in four major Nigerian languages, this virtual extesnion agent provides personalized advice on crop management. pest control, soil health,and market trends. It also integrates real-time weather data and climate predictions, offering adaptive strategies to build rsilience against climate change impacts.
12_outputs: The telephone-based technology allows small holder farmrs access to extension services and information about market trends to impact them mitigate the impact of climate change.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/2/2024
19_date_acq_dev_began: 11/2/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 72062020C00001
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: No – agency does not have access to source code.
40_has_ato: No
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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.