Coronavirus: Using AI to Understand Policy Effects on the Economically Marginalized during Pandemics
AI Challenge started! Preliminary end date: May 19th
Enabling governments to design data-driven policies to deal effectively with pandemics like the Coronavirus. In this Omdena Challenge, 65 AI experts, data scientists, and domain experts from more than 20 countries apply AI and policy research to answer the following prevalent questions:
1.5 billion people across the globe urged to stay home to halt coronavirus spread. What will that mean for the economically vulnerable? Are government policies taking into account the poorest and the most vulnerable in an effective manner?
Among the partners for this challenge are the renowned AI for Good Summit, Labelbox, AI for Peace, and more.
Want to partner up?
If you want to join the challenge as a funding, technology, or media partner, contact us here.
The problem: Coronavirus called a pandemic
A pandemic is a disease that is spreading in multiple countries around the world at the same time.WHO called on governments to change the course of the Corona outbreak by taking “urgent and aggressive action”. Many governments took strict measures such as closing borders, quarantining entire cities, and implementing travel bans.
While these measures may be effective in containing the spread of disease they may also have indirect mid- to long-term effects on the economy and adverse effects on the well-being of people, especially the poor and economically marginalized.
The challenge goal: Data-driven policymaking
In this two-month Omdena AI challenge, we aim to help governments and policymakers to make data-driven decisions in order to deal with pandemics and cover some of the following topics.
When travel is restricted, schools closed, events canceled, and communities put into quarantine, individuals and businesses in those ecosystems lose their source of income. How does that loss of income impact the health and financial stability of those individuals?
What we will build
Leveraging the power of global collaboration and our unique Collaborative AI processes, the goal is to build AI models that reveal the effects of specific policy decisions being made by governments and institutions on the economically marginalized, especially working in the informal sector.
In this way, institutions can identify the most effective ways to deal with future pandemics to minimize economic impact and human deaths not only in the short term but also in the midterm.
To support our AI experts and data scientists, domain experts from the World Health Organization, The World Bank, European Commission, UNICEF USA, are joining the challenge.
This challenge is hosted with our friends at
The AI for Good Global Summit is THE leading action-oriented, global & inclusive United Nations platform on AI. The Summit is organized by the ITU with XPRIZE Foundation, in partnership with UN Sister agencies, Switzerland and ACM.
Foundation FruitPunch AI is intended to be the knowledge platform for Artificial Intelligence in the BrainPort region.
The AI for Peace is San Francisco based nonprofit organization focused on studying and understanding the impacts of artificial intelligence (AI) and related exponential technologies on society.
Their vision is a future in which AI benefits peace, security, and sustainable development and where diverse voices influence creation of AI and related technologies.
Labelbox solves the problem of taking artificial intelligence and machine learning initiatives from research and development into production. In addition to working directly with their customers, Labelbox's main product is a platform that makes it easy to create and manage labeled data, enabling rapid deployment of artificial intelligence applications.
The Project Work Groups (PWG) is an upcoming for-profit business entity established to support and provide administrative oversight and technical program assistance to programs/projects in Africa, Middle East, and Asia - working with agencies and international institutions committed to a Collective Action Plan (CAP) approach.