GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Machine Learning for Peace
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Democracy, Human Rights, and Governance
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: DRG is partnering with a seven-member consortium, including researchers at Duke University’s DevLab, to build an Artificial Intelligence (AI) tool that can forecast closing civic spaces -- patterns of events that result in curtailed freedoms and impaired democracy.
12_outputs: Event labels, forecasts
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/10/1905
19_date_acq_dev_began: 7/10/1905
20_date_implemented: 7/14/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: GS-10F-0294V/7200AA23M00014
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: Long-term impacts of land-use/land-cover dynamics on surface water quality in Botswana’s reservoirs using satellite data and artificial intelligence methods: Case study of the Botswana’s Limpopo River Basin (1984-2019)
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Inclusive Growth, Partnerships, and Innovation
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: For water supply, semi-arid Botswana relies on the reservoirs within the Botswana’s LRB. Reservoirs are particularly susceptible to the negative impacts of land-use and land-cover (LULC) activities and runoff because of their complex dynamics, relatively longer water residence times, and their role as an integrating sink for pollutants from their drainage basins. Despite these interrelationships and significance in regional and global economic stability, land and water (L-W) are often treated in “silos”. To understand the complex L-W nexus within the LRB, this study will use data-driven artificial intelligence for quantitative determination of the relationships between LULC change, together with socioeconomic development indicators and climate change, and their impacts on water quality and availability within the basin, both for 1984-2019 and to predict future scenarios (2020-2050). To advance data acquisition for LULC analysis and climate change, the study utilizes optical Earth-observation and meteorological satellite data. To provide near real-time and cost-effective approach for continuous monitoring of reservoir water quality within the basin, the study will develop empirical models for water quality estimation and water quality index mapping using 35-years of in-situ water quality measurements and water spectral observations using drone-borne spectrometer and optical satellite imagery through regression modeling and geospatial methods.
12_outputs: Reports, visualizations
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 7/13/1905
19_date_acq_dev_began: 7/13/1905
20_date_implemented: 7/13/1905
21_date_retired: 7/15/1905
2_use_case_name: Morogoro youth empowerment through establishment of social innovation (YEESI) lab for problem-centered training in machine vision
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Inclusive Growth, Partnerships, and Innovation
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: The project established the YEESI social innovation lab at Sokoine University of Agriculture to become a center of excellence for advancing ICT education in Tanzania. Students at YEESI are trained in Machine Learning (ML), Machine Vision (MV), and Natural Language Processing (NLP) applications to address the most pressing agricultural challenges in the country. 
12_outputs: Over 650 students received training at YEESI during the project. The Lab established the first comprehensive open-source machine vision dataset for central regions of Tanzania and worked with agriculture start-ups and NGOs to improve sustainable agricultural practices and economic growth.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 7/13/1905
19_date_acq_dev_began: 7/13/1905
20_date_implemented: 7/13/1905
21_date_retired: 3/1/2024
2_use_case_name: Breakthrough RESEARCH’s Social Media Listening
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Global Health (GH)
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Social media listening draws on machine learning to synthesize and organize the vast quantities of data shared over social media platforms. Breakthrough RESEARCH carried out social listening on 12,301 social media posts in Nigeria to explore how gender-related online conversations manifest themselves and whether they have changed in the last five years. Using Crimson Hexagon’s machine learning algorithm, “Brightview,” publicly available social media content originating in the countries of interest was scraped by the algorithm, for posts relevant to RH/FP and youth. The resulting social media posts were then classified by topic, using language detected in the content. This provided a dataset categorizing conversations into overarching topics, allowing analyses to uncover key trends in topic specific conversation volume, insights about misinformation, attitudes and social norms, and more. The machine learning algorithm was able to identify relevant social media content. The 12,301 social media posts were qualitatively assessed and categorized, allowing researchers to monitor and track social media conversations far more expansively than allowed by research methods more traditionally used in public health and SBC programs.
12_outputs: Reports, visualizations
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 7/14/1905
19_date_acq_dev_began: 7/14/1905
20_date_implemented: 7/14/1905
21_date_retired: 7/15/1905
2_use_case_name: Serbia: AI predictions for the utilization of hospital beds
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Global Health (GH)
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: AI technology was used to predict bed occupancy at hospitals with MoH data from 2019, with an overall median error by department around 20%. This was a proof-of-concept model developed at the request of the Institute of Public Health (IPH) Batut to understand how AI can work and the value add. CHISU was asked to subsequently focus on a different use case (waiting list optimization for scheduled imaging diagnostics services, specifically CT and MRI), which is considered higher priority to demonstrate the implementation of the national AI strategy and the effect of AI in data use for decision making by the government, and will be addressed in the 2023-4.
12_outputs: Time series
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 7/14/1905
19_date_acq_dev_began: 7/14/1905
20_date_implemented: 7/14/1905
21_date_retired: 7/15/1905
2_use_case_name: Qure.AI integrated in mobile X-ray screening to improve community TB case finding
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Vietnam
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The USAID Support to End TB project applies Qure.AI in mobile X-ray van to improve the quality of X-ray screening under community active TB case finding efforts in Vietnam. AI system is also critical to shorten the screening time and improve TB detection accuracy.
12_outputs: Image classification
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 7/12/1905
19_date_acq_dev_began: 7/13/1905
20_date_implemented: 7/13/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72044020CA00002
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.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
2_use_case_name: AI for Predictive Analysis of patients on HIV treatment
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/India
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: JHU ACCELERATE used Artificial Intelligence (AI)/Machine Learning for identifying People living with HIV(PLHIV) on treatment who are at risk of becoming lost-to-follow-up (LFU). Identifying the PLHIV on treatment who are at risk of becoming LFU will allow the project team to provide pre-emptive counselling and intervention to target/customize approaches patients who could become LFU.
12_outputs: Patient risk scores
16_dev_stage: Retired
17_impact_type: Both
21_date_retired: 7/14/1905
2_use_case_name: Private Sector Engagement Evidence Gap Map Analytical Pilot
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Inclusive Growth, Partnerships, and Innovation
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The Private Sector Engagement Evidence Gap Map (EGM) Analytical Pilot examined the viability of using natural language processing to facilitate analysis of the EGM documents at scale (https://crcresearch.github.io/usaid-pse-egm/#/egm). This use case should be regarded as exploratory in nature.
12_outputs: Reports, visualizations
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 7/14/1905
2_use_case_name: Risk-based modeling of poacher movements in protected areas
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Southern Africa
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of the risk-based modeling of poacher movements in protected areas AI use case is to predict the risk of poaching activity in a given location within a national park/ conservation area. This model is intended for use by protected area managers to allocate resources based on poaching risk. The model can present risk (outputs) based on 1) an inferred historical binary prediction (threat/ no threat); 2) a risk category or converted probability (low/ medium/ high); or 3) by predicting specific poaching activities near a given location (e.g., types of activities: footprints; snares; wildlife carcasses; etc.). At the current stage/ iteration, inputs include a variety of data that can be grouped into the following categories: animal tracking data; terrain/ habitat/ infrastructure data; park/ conservation area historical data; and spatial/ temporal data.
12_outputs: Annotated maps
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 7/14/1905
2_use_case_name: CARI/El Salvador: Risk factors at community level that cause Internal Displacement
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Conflict Prevention and Stabilization
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Through this activity, USAID/CPS/OTI's Central America Regional Initiative (CARI)/El Salvador program will support Cristosal's (a human rights organization) efforts to better understand how extortion impacts internal displacement. A highly-qualified research team made up of forensic technicians and specialists, with the required professional certifications to use the ethics data analytics tool, will coordinate with Cristosal to interview 60 families from Panchimalco and other communities that have been internally displaced to research the dynamics of violence and social norms in the communities that have motivated the population to migrate internally. The data analytics tool will eliminate common biases that many surveys encounter in gathering information, which will mitigate collection and analysis errors. The grantee will analyze the data collected to identify the potential utility of early warning systems for IDPs, and how community based responses could support reducing extortion-related violence. The grantee will offer recommendations on how to operationalize their findings through programming.
12_outputs: Interview transcripts
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 2/1/2022
2_use_case_name: Chemonics International Inc.'s "Disinformation Monitoring and Sentiment Analysis"
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Conflict Prevention and Stabilization
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: The Transition to New Sudan program, through a program subcontractor, uses AI technology to monitor social media posts for disinformation, and to monitor citizen sentiment analysis.
12_outputs: Reports, visualizations
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 7/16/1905
2_use_case_name: PEER 8-161: A wood species identification tool to aid in compliance and enforcement of Peruvian timber regulations
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Inclusive Growth, Partnerships, and Innovation
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: The Xylotron, created by the U.S. Forest Service's Forest Products Laboratory, uses artificial intelligence to rapidly identify wood species with the goal to identify timber prohibited from logging under national law or the Convention on the International Trade of Endangered Species (CITES).
12_outputs: The Xylotron uses AI to identify the timber species in a sample by matching a scanned image with the database of DNA data. The research team added 35 Peruvian species to the Xylotron's database as part of this research project.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 12/1/2021
2_use_case_name: PEER 9-452: Long-term impacts of land-use/land-cover dynamics on surface water quality in Botswana’s reservoirs using satellite data and artificial intelligence methods: Case study of the Botswana’s Limpopo River Basin (1984-2019)
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Inclusive Growth, Partnerships, and Innovation
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The PEER project used data-driven AI for quantitative determination of the relationships between land-use and land-cover (LULC) change, socioeconomic development indicators, and climate change and their impacts on water quality and availability within the Limpopo River Basin.
12_outputs: The researchers developed machine-learning models for mapping and quantifying the spatial-temporal LULC change patterns, modeling water quality and quantity in the dam catchments under climate variability and socio-economic factors.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 3/1/2024
2_use_case_name: BA Cote d'Ivoire Public Social Media Data
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Global Health (GH)
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: For Breakthrough ACTION Côte d’Ivoire, we use a software called Meltwater (www.meltwater.com) to extract and organize public social media data from Facebook, Twitter, web-based news, blogs, and similar sources. Meltwater has web crawlers that extract data in real-time (every 2-6 minutes). They have full firehose access to Twitter and Reddit and access to Facebook and other social sources in line with the API restrictions of those companies. We also have a CrowdTangle (www.crowdtangle.com) account, which is Facebook’s (Meta’s) tool for researchers and for social good. Finally, we have a custom Python application which pulls public posts from the Facebook API (through CrowdTangle) and uses machine learning to optimize “topics” from the text data (i.e. groups of terms that reflect certain subjects of discussion on Facebook). In all these cases, artificial intelligence takes the form of natural language processing and image recognition, for example, sentiment analysis and identifying key terms and topics from unstructured data.
12_outputs: Reports, visualizations
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 3/1/2023
2_use_case_name: Smart Supervision Platform - Superintendencia Financiera de Colombia (Colombian Financial Regulator)
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: This platform collects data from customer and financial intermediaries about financial products complaints. The system processes all these data and make analysis anticipating trends and other types of components must be improved by the financial institutions and give tools for facilitating the decision – making for supervision activities. The functionality of performing data analysis is based on the information collected using panels control and indicators, it will allow classify the information of the different dimensions, analyzing them statistically, in order to be able to determine trends over time, by entities, regions, and the improvement of complaint or claim attention processes.
12_outputs: Time series predictions, visualizations
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 7/14/1905
2_use_case_name: Feed the Future Innovation Lab for Applied Wheat Genomics
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Resilience, Environment, and Food Security
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The AI simultaneously utilizes multi-modal deep learning to leverage both genotype and phenotype measurements by fusing genetic variants with multiple sources of data collected by unmanned aerial systems to predict wheat yield in seen and unseen environments.
12_outputs: Testing (the Innovation Lab ended and there is no output to report)
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 7/15/1905
2_use_case_name: Market segmentation
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Global Health (GH)
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Use of analytical approaches to increase the understanding of the market size and characteristics for private HIV treatment and services. Utilizes existing data to profile people living with HIV/AIDS (PLHIV) accessing HIV services and treatment based on willingness to pay (WTP) to create profiles incorporating demographic dimensions, geographic dimensions, and temporal dimensions to help define the size of the market and for HIV treatment services for PLHIV. Data sources include geospatial datasets, market data on comparable products, and micro-economic data. The use of novel data sources creates outputs in the form of patient profiles or segments at the global, regional, and/or country level to better understand which patients would be willing to pay for HIV treatments or services. Use of the market segmentation activity with development of a country-agnostic approach helps profile and estimate the size of a specific population group of PLHIV based on their willingness to pay for HIV services that can be applied to any PEPFAR-priority country context.
12_outputs: Reports, visualization
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 7/16/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 7200AA19CA0004
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: Climate-based malaria forecast
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Global Health (GH)
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The climate-based model takes malaria confirmed cases and eco-climatic data to forecast the number of malaria cases/incidences up to 2 months ahead.
12_outputs: Time series predictions 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2022
2_use_case_name: Covid Decision Support
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/India
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: USAID-supported TRACE TB led by Wadhwani Institute for Artificial Intelligence supported the Municipal Corporation of Greater Mumbai (MCGM) with eight forecasts commissioned during June 2020 to January 2021. These predictive modelling and analytics forecasts, and recommendations supported the MCGM public health authorities and led to an additional over 2,500 ICU beds and over 50,000 quarantine beds months in advance of the second COVID-19 wave. Based on insights from data analysis the forecast suggested to increase case discovery by ~25% in under-tested wards. Similar support was provided to Jharkhand (six districts) from September 2020 - January 2021 with three forecasts and data analytics. The model received appreciation letters from the State Government of Jharkhand and Maharashtra.
12_outputs: Time series predictions
16_dev_stage: Retired
17_impact_type: Both
21_date_retired: 7/14/1905