CAIAC alliance launched to combat COVID-19 with data
An alliance has been launched with the aim of providing an AI-supported decision-making platform to the world’s policy leaders in the face of COVID-19.
The alliance, known as the Collective and Augmented Intelligence Against COVID-19 (CAIAC), has been formed by the Future Society and Stanford Institute for Human-Centered Artificial Intelligence (HAI), with the support of UNESCO and the Patrick J. McGovern Foundation.
CAIAC is to establish an advisory group, inviting experts from UNESCO, UN Global Pulse and other UN entities, to structure the ever expanding data on global health, both social and economic, to better inform policy makers, as well as healthcare leaders and the scientific community.
On the alliance’s website, Cyrus Hodes, Co-founder & Chair of the AI Initiative at The Future Society, said: “The pandemic has highlighted how vulnerable we are as a civilization. It has also accelerated innovation and global cooperation with the blossoming of thousands of bottom-up initiatives to find solutions. Creating a coherent, truly holistic, knowledge platform for policy makers to navigate the flow of information has become a necessity. Fortunately, modern technologies such as AI can be used to harness vast amounts of disparate data and produce meaningful insights. CAIAC will build this ground-breaking decision-making tool for global leaders to help solve the pandemic and accelerate the economic and social recovery.”
With the world having been somewhat blindsided by the emergence of COVID-19, in a press release the alliance pointed out the fact that data is being created by research institutions, think-tanks, and NGOs with no trusted filter for their analyses and models. Hence, the organisation is intending to build a dynamic platform in collaboration with private sector partners such as C3.ai, stability.ai, Element AI, Axis, GLG, and Planet to build artificial intelligence into the platform.
The minimum viable product will be focused on three use cases: tracking and tracing contagion chain, identifying inaccurate COVID-19 information, and finding the areas most affected by recurrences of the pandemic.
