GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: EYouth: Learning and training opportunities for refugees and host communities
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: EYouth: Provides learning and training opportunities for refugees and host communities through their AI-based tech learning platform. The venture offers different types of online learning paths through an interactive methodology that helps young people acquire the necessary skills and find the right job. Funded through WFP Innovation Accelerator
12_outputs: Customization of learning content
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/30/2024
19_date_acq_dev_began: 4/30/2024
20_date_implemented: 4/30/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA24IO00037
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: Ignitia: AI-powered weather forceasts for smallholder farmers
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: Ignitia: Uses a reliable and accessible SMS forecast and weather intelligence platform to provide smallholder farmers in the tropics with more efficient weather forecasts. Delivers localized forecasts to farmers through an advanced physics and AI predictive model optimized for the tropics that is twice as accurate as global models. To date, Ignitia has provided services to 2.7 million smallholder farmers, of which approximately 700,000 are recurrent. WFP supports Ingitia with grant funding to further its agribusiness models. This will help them improve agricultural yield. Funded through WFP Innovation Accelerator
12_outputs: Short text messages
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/30/2024
19_date_acq_dev_began: 4/30/2024
20_date_implemented: 4/30/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA24IO00037
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: SKAI: Large-scale assessment of building damage
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: SKAI: An open-sourced tool that uses satellite imagery and AI to automatically assess building damage on a large scale. Enables real-time insights and actionable intelligence for effective decision-making during disaster response. Fuses cutting-edge machine learning algorithms and vast satellite data. Helps organizations make data-driven decisions with unprecedented precision and speed, therefore speeding up humanitarian assistance to affected households after disasters. Funded through WFP Innovation Accelerator
12_outputs: Maps, reports
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/30/2024
19_date_acq_dev_began: 4/30/2024
20_date_implemented: 4/30/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA24IO00037
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: NEMO: Real-time categorization of hotline calls
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: NEMO: Aims to streamline and improve the quality of collected feedback and complaints by using AI to provide real-time categorization recommendations of hotline calls. Employs customized open-source AI algorithms and off-the-shelf AI services. This AI-natural language processing combination improves data collection efficiency through automated speech-to-text for feedback call transcriptions, real-time call categorization assisting hotline operators, and error identification. Funded through WFP Innovation Accelerator
12_outputs: Categorization of calls
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/30/2024
19_date_acq_dev_began: 4/30/2024
20_date_implemented: 4/30/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA24IO00037
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: WFP AI Sandbox: Platform to innovate, pilot, and scale AI models and use cases
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: AI Sandbox: Aims to create a collaborative environment that provides WFP colleagues and partners wtih a platform to experiment, innovate, pilot and scale AI models and use cases. Empowers WFP teams to work efficiently and impactfully at scale. Enables the scaling of AI within WFP, piloting impactful use cases with scalability potential. Invests in talent, builds an ethical governance framework, and establishes a responsible-use roadmap. Funded through WFP Innovation Accelerator
12_outputs: Varies by specific use case
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/30/2024
19_date_acq_dev_began: 4/30/2024
20_date_implemented: 4/30/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA24IO00037
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: Enhancing Smart Agriculture in Rural Africa
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: Enhancing Smart Agriculture in Rural Africa: Eliminating post-harvest loss Rooted in real-time data collection, it leverages TinyML and AI to provide actionable insights for farmers. Their system emphasizes early pest and disease detection, ensuring reduced crop losses. Funded through WFP Innovation Accelerator East Africa Regional Hub
12_outputs: Reports, visualizations
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/30/2024
19_date_acq_dev_began: 4/30/2024
20_date_implemented: 4/30/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA24IO00037
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: Emata: Affordable digital finance for farmers
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: Emata: Affordable digital finance for farmers Emata is a Ugandan fintech and licensed microfinance institution, leveraging technology and partnerships with agricultural cooperatives to offer farmers affordable, digital financing. In place of collateral requirements, the company creates AI-powered alternative credit scores based on data points like a farmer’s delivery history. Emata encourages farmers to grow their farms into businesses and invest in a productive future. Funded through WFP Innovation Accelerator East Africa Regional Hub
12_outputs: Credit scores
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/30/2024
19_date_acq_dev_began: 4/30/2024
20_date_implemented: 4/30/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA24IO00037
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: SoilPro 2.0: Advancing impact monitoring & monetization
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: SoilPRO 2.0, a collaboration with SoilWatch, pioneers in science-based ecosystem and food system restoration and transformation, aims to integrate further innovative environmental monitoring into AIMS by using satellite imagery, peer-reviewed models, machine learning, and in-field testing-based validation. Utilizing the robust data and analysis provided by AIMS for evaluating the impact of our NBS, the project can advance every project activity in the region through a funding and carbon financing toolkit. Funded through WFP Innovation Accelerator East Africa Regional Hub
12_outputs: Reports, visualizations
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/30/2024
19_date_acq_dev_began: 4/30/2024
20_date_implemented: 4/30/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 720BHA24IO00037
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: RAG system for FEWS NET food security reports
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The FEWS NET Data, Learning and Communications Hub is developing an AWS SageMaker backed, Retrieval-Augmented Generation (RAG) model utilizing 40 years of historical FEWS NET food security reports. This allows analysts to use natural language to quickly search and retrieve relevant information, with links to the original reports. This model ensures that the wealth of historical FEWS NET food security analysis is accessible. The result is a more informed, data-driven foundation for future project decisions.
12_outputs: Text responses to queries
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 7200AA24F00008
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: Generating SQL queries from natural language
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The FEWS NET Data, Learning and Communications Hub is developing an application that  allows select users to generate SQL queries using natural language, based on a modified and secure database schema. By allowing analysts without SQL expertise to efficiently extract data from the FEWS NET Data Warehouse, this tool enhances accessibility and streamlines data retrieval, making critical insights more readily available for decision-making.
12_outputs: SQL queries
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 7200AA24F00008
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: API queries from natural language prompts
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The FEWS NET Data, Learning and Communications Hub is developing an application that enables a large language model (LLM) to interpret the FEWS NET Data Warehouse OpenAPI specifications, allowing it to generate API queries from user-provided natural language prompts. The LLM can then either extract data from the API or answer questions based on the API responses, facilitating seamless data access and interaction for users without in-depth API knowledge.
12_outputs: Formatted API outputs
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 7200AA24F00008
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: Interactive exploration of FEWS NET food security reports
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Humanitarian Assistance
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The FEWS NET Data, Learning and Communications Hub is implementing a dashboard application developed by the International Center for Tropical Agriculture (CIAT) that uses natural language processing (NLP) to allow uses to interactively explore FEWS NET's 40 years of historic food security reporting to understand the evolution of drivers and trends in food security in the countries that FEWS NET covers.
12_outputs: Reports, visualizations
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 7200AA24F00008
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.