GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Using technology to combat violence against female refugees
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Using technology to combat violence against female refugees, ActionAid in London (UK): A low-cost mobile platform for at-risk women and girls to offer essential information on rights, services, safe spaces, and GBV referrals. It enables incident reporting and risk mapping in urban areas. Pilot 1 will launch in collaboration with a Mobile Network Operator and Women's Protection Action Groups. Funded through Humanitarian Grand Challenge
12_outputs: Reports, maps
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/26/2023
19_date_acq_dev_began: 3/14/2024
20_date_implemented: 3/14/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA23GR00104
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 resources provisioned by USAID
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: AutoAnthro: 3D scanning for improved malnutrition assessment in conflict areas
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: AutoAnthro: 3D scanning for improved malnutrition assessment in conflict areas, Body Surface Translations, Inc.: Body Surface Translations' AutoAnthro is a cutting-edge 3D scanning system revolutionizing child anthropometry. It swiftly captures precise digital measurements, enhancing outcomes for malnutrition at both individual and population levels. AutoAnthro extracts crucial data, measuring length/height, mid-upper arm circumference (MUAC), and head circumference for children aged 0-5 years. Funded through Humanitarian Grand Challenge
12_outputs: Body measurements of children
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/26/2023
19_date_acq_dev_began: 3/14/2024
20_date_implemented: 3/14/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA23GR00104
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: age, 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 resources provisioned by USAID
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 learning enabled safe water optimization tool for humanitarian response
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Emergency Management
10_commercial_ai: None of the above.
11_purpose_benefits: Machine learning enabled safe water optimization tool for humanitarian response, York University: This project aims to create a cloud-based tool using AI to optimize safe water practices. By analyzing existing water quality data, it generates customized chlorination instructions for specific field sites worldwide, ensuring safe drinking water based on evidence and site conditions. Funded through Humanitarian Grand Challenge
12_outputs: Text
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/26/2023
19_date_acq_dev_began: 9/24/2024
20_date_implemented: 9/24/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA23GR00104
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 resources provisioned by USAID
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: Combatting health misinformation in Yemen
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Combatting disinformation in conflict zones, The SecDev Foundation: SecDev Foundation collaborates with local partners in Yemen to empower conflict-affected communities in combating harmful health misinformation.They provide guidance through trusted local networks via various channels like emergency alerts, online campaigns, and radio bulletins to ensure accurate information reaches those in need. Funded through Humanitarian Grand Challenge
12_outputs: Text
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/26/2023
19_date_acq_dev_began: 3/14/2024
20_date_implemented: 3/14/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA23GR00104
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 resources provisioned by USAID
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-enabled air raid sirens and war crime documentation
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Emergency Management
10_commercial_ai: None of the above.
11_purpose_benefits: Hala Systems Inc.: Hala will utilize a network of air-raid sirens administered by civil society, combined with predictive algorithms, training and community engagement efforts to monitor other forms of violence (e.g. artillery, mortars), It will employ blockchain to generate immutable evidence of war crimes. Funded through Humanitarian Grand Challenge
12_outputs: Structured data
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/26/2023
19_date_acq_dev_began: 3/14/2024
20_date_implemented: 3/14/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA23GR00104
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 resources provisioned by USAID
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 tools for volunteer mapping
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Missing maps project, Humanitarian OpenStreetMap Team United States: Missing Maps, a global collaboration, mobilizes volunteers to map uncharted areas through user- friendly tools and open-source apps. Remote volunteers use satellite imagery to create base maps, then local volunteers and agencies refine and verify the details. This hyper-local data supports life-saving crisis response programs. Funded through Humanitarian Grand Challenge
12_outputs: Mapping tools
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/26/2023
19_date_acq_dev_began: 9/24/2024
20_date_implemented: 9/24/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA23GR00104
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 resources provisioned by USAID
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: Chatbot to provide accurate COVID-19 information
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Fighting COVID-19 misinformation with language technology, Translators without Borders US Inc.: The chatbot engages people in their native language, providing accurate pandemic information and informing targeted responses to misinformation. Currently in the Democratic Republic of the Congo,TWB plans to expand to more languages and countries. This innovative solution, accessible via platforms like WhatsApp and Messenger, ensures reliable COVID-19 information reaches vulnerable communities in a language they comprehend. Funded through Humanitarian Grand Challenge
12_outputs: Chatbot interface
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/26/2023
19_date_acq_dev_began: 3/14/2024
20_date_implemented: 3/14/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA23GR00104
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 resources provisioned by USAID
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: Imagery authentication from conflict zones
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Snap truth to power, Sealr: Sealr, a mobile app, combines AI and blockchain to authenticate imagery from conflict zones.This
 empowers vulnerable populations to report needs securely. As the first verified image-capture app for humanitarian crises, Sealr has the potential to revolutionize a sector often hindered by inefficient feedback systems. Funded through Humanitarian Grand Challenge
12_outputs: Image metadata
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/26/2023
19_date_acq_dev_began: 3/14/2024
20_date_implemented: 3/14/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA23GR00104
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 resources provisioned by USAID
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: Tracking COVID-19 misinformation on social media
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Countering COVID-19 misinformation in areas affected by conflict, Murmurate: Murmurate tracks how misinformation spreads in communities via social media. Using Computer Vision and Natural Language Processing, Murmurate maps COVID-19 conversations online, identifying misinformation that delivers false and damaging public health statements.These insights drive the production and distribution of OVID-19-related counter-messaging. Funded through Humanitarian Grand Challenge
12_outputs: Reports, visualizations
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/26/2023
19_date_acq_dev_began: 3/14/2024
20_date_implemented: 3/14/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA23GR00104
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 resources provisioned by USAID
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: Bambara language processing tool
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Mali
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: The Bambara language processing tool is being designed to fill a gap that current technology lacks in tracking narratives, usually originating in French here in Mali. Through various platforms, messages are replicated and shared without verification, and at the moment, we're only able to gauge how far French narratives are spreading. This will enable us to track social media narratives in Bambara, a language spoken by 90% of Mali's population.
12_outputs: Reports, visualizations
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
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.
40_has_ato: No
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No resources provisioned by USAID
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: Repair Spend
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: ARS: Agricultural Research Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The intended purpose of this model is to review financial documents and then classify each expense as money spent on "facility repairs" or "not facility repairs". The expected benefits include reduction of manual hours identifying the types of transactions.
12_outputs: The output of the model is a recommendation of which financial transactions should be identified as "repair" expenses.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2019
19_date_acq_dev_began: 10/1/2019
20_date_implemented: 6/9/2020
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: Developed by REE-Analytics within EDAPT to refresh Impala tables and visualized on the Tableau Server
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Approximately 14,000 financial transactions were used to train the model and finetune its parameters. Approximately 3,500 financial transactions were used to test the performance of the final model.
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: Enterprise Data Analytics Platform & Toolset (EDAPT, Impala, Tableau)
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 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.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: ARS Project Mapping
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: ARS: Agricultural Research Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The intended purpose of this model is to process research plans from various research program portfolios in the Agricultural Research Service (ARS) to find patterns and opportunities between projects. The expected benefits include decreasing the time that humans would spend to manually read, pull out key terms, and group the projects by topic. The model may also find patterns that a human might miss.
12_outputs: The model outputs groups of similar projects and project terms. The output includes metrics (silhouette scores, term rank, importance scores) that show how well the projects and terms in a group match.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2020
19_date_acq_dev_began: 1/1/2021
20_date_implemented: 5/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: 1232SA21F0067
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is a collection of project plans written by scientists, roughly 600 text documents. The texts are related to publicly available 5-year action plans. 
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: Enterprise Data Analytics Platform & Toolset (EDAPT)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
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: 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.
51_extension_request: No – Agency did not request an extension for this use case.