Demo Day and Panel Recap
The Good, the Bad, and the Ugly of Building the Future AI-enabled NGO
CEO, Cloudera Foundation
Claudia Juech will introduce the event by addressing challenges NGOs face with AI development. She will draw from her rich experiences leading Cloudera Foundation where a diverse team of professionals across the nonprofit and private sector realize the potential of data analytics and machine learning for social good.
Head of Technology for Development & Innovation, Save the Children
Emerging Technology Advisor, USAID
Director, Data Science for Zero Hunger Lab, Former CEO CARE Nederland
The case studies
The Good: Preventing sexual harassment through a safe-path finder algorithm
“UN Women states that 1 in 3 women face some kind of sexual assault at least once in their lifetime.”
The project team leveraged open-source data and the partner organization´s database to built heatmaps and calculate safe routes using Nearby Search, Directions API and Grid Coverage techniques. Part of the solution is a sexual harassment category classifier with 93 percent accuracy and several models that predict places with a high risk of sexual harassment incidents.
Project host: Safe City India
Understanding the Sentiments, Thoughts, and Aspirations of Young People
A global team of up to 50 changemakers collaborated to capture and understand what young people (age 10-24 yrs) to gain insights from publicly available sources such as Twitter, Reddit, and Instagram, and other sources like Google trends and UN Data, to understand the sentiment of young people, converting this analysis into diagrams, word clouds, and additional insights on young people’s perspectives.
Project host: Fondation Botnar
Disaster Response: Predicting relief packages
34 AI experts and data scientists worked with the World Food Programme to build a prototype to predict affected populations and create customized relief packages for disaster management. The tool looks for various different features such as days to be covered, the number of affected people, pregnancies, kids, etc.
Project host: WFP Innovation
The Bad: Preventing child sexual abuse in online chats with an imbalanced dataset
Together with the Zero Abuse Project, an award-winning non-profit committed to the elimination of child sexual abuse, Omdena’s AI community applied Natural Language Processing (NLP) to find ways to prevent online sexual abuse.
The project team worked with a large and unbalanced data set with very few certain cases of predatory behavior. Therefore, predictions made by the algorithms require succinct validation.
Analyzing the importance of sex education via social media
A society-based analysis regarding the importance levels and effects of sex education is missing in many countries. Together with Polish NGO SexedPL 36 collaborators applied AI to understand the effects of sex education in Polish society.
The team used NLP to analyze frequently discussed sexually-related topics in Polish online forums. In addition, an influencer survey was conducted.
Building a Post-traumatic-stress-disorder (PTSD) risk classifier with no initial data
When Colour the World reached out to us to build a solution for PTSD assessment in low-resource settings, they did not provide a data set to begin with. Within eight weeks, a team of 32 Omdena Collaborators prototyped a machine learning driven chatbot for PTSD assessment in war and refugee zones. Through collaborative efforts the community identified sources with suitable patient data and transformed them into an intelligent chatbot that leverages natural language processing to assist doctors in need.
Watch the recording