AI for Humanitarian Action projects

The global pandemic has compounded existing concerns around domestic abuse leading to a significant increase in cases. Extended periods of lockdown under stressful conditions have highlighted the fact that not everyone can find safety at home. In 2021, the Governor of Puerto Rico declared a state of emergency due to gender-based violence.

Coordinadora Paz para las Mujeres and Seguro Project created a chatbot to provide a confidential and interactive interface for accessing support and guidance in a time of need. It helps victims of gender-based-violence in Puerto Rico to identify local resources and information relevant to their individual situation.

Data collection for large-scale development programs through digital devices may not always be feasible. The challenge was to collect paper-based forms from an actual field project, scan, and label them, and use this dataset to train an Optical Character Recognition (OCR) model to digitize more paper forms.

In partnership with Gram Vaani, IIT developed and deployed Smartforms, a tool for automated digitization of paper forms through OCR and Optical Mark Recognition (OMR) techniques.

Investigation teams at UNITAD collect a large amount of evidence from recovered devices such as images and text. Typically, content moderators manually examine each file and tag it based on their interpretation of the content. This process was time consuming—a particular challenge for UNITAD given the significant amounts of digital devices and other evidence that are collected by the team on a regular basis.

UNITAD built a platform for analyzing evidence at scale, allowing for secure upload and preparation of raw data for processing. Azure cognitive service then quickly “looks inside” the recovered files to identify relevant information and label the data with insights, enabling the investigative teams to process the evidence more quickly and effectively.

Previous techniques to reunite family members separated during the Syrian conflict were hindered by incomplete or unknown information, resulting in low frequency in matching missing people.

A facial recognition model was developed to match a photograph of a separated person with a recently registered profile in the Turkish Red Crescent system to provide a list of potential matches to the caseworker as additional input to the reunification process.

The use of cluster bombs is prohibited under international law, due to their indiscriminate effect on civilian casualties. Identifying the use of cluster bombs can be difficult to confirm, despite often overwhelming anecdotal accounts alleging their presence.

Cluster bombs produce a distinctive popping sound when they explode. Benetech developed an acoustic-based weapons classification system to identify the likelihood of a cluster bomb being used, based on a recording of a given explosion.

In the last ten years, 2 billion people were affected by disasters. In 2017, 18 million people were displaced due to weather related disasters alone. Many of these disaster-affected areas are literally “missing” from existing maps, making it difficult for first responders to prepare and deliver relief programs.

The Humanitarian OpenStreetMap Team is using machine learning resources from Microsoft to generate datasets of buildings in Uganda and Tanzania to improve the mapping process for project managers and cartographers with AI assistance tools. By identifying buildings at a faster pace than manual mapping, the solution helps add underserved communities to the map.

The ongoing violence in Syria continues to displace millions. To aid in understanding and reducing the conflict, The Carter Center compiles data from conflict incidents mostly using the ACLED database, and publishes weekly reports used by the UN, diplomatic actors, and conflict responders. However, the datasets require manual, time-consuming data cleaning to classify all the information.

Microsoft has partnered with The Carter Center to build a machine learning text analysis tool that will aid in automating the weekly repetitive data cleaning to more efficiently analyze and share information. By minimizing the human supervision cost, conflict researchers can dedicate more time to understanding the trends and key insights needed to support their peacebuilding and humanitarian efforts.

Previously, an attorney paired with a detained immigrant conducted an initial intake and entered data by hand. Due to the high volume of cases and limited staff time, only a small portion of collected information was entered electronically, delaying processing time and limiting operational intelligence.

A customized Optical Character Recognition (OCR) technology and case management platform was built that scans paper intake forms and sorts standardized fields of data into databases remotely for legal service providers in California.

Current disaster vulnerability assessments and hazard warnings for communities in India offer vague predictions with high variability because they do not consider individual, community, and regional attributes in their assessment.

By incorporating attributes unique to each community, such as building materials and topography, SEEDS India has created more accurate community-specific models to predict vulnerability to natural disasters, allowing communities to be better prepared in advance and develop custom response plans.