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AI Can Help To Inform Coronavirus Policy

An Interview with Dr. Deborah Duong, Director for AI Development at Rejuve.

Dr. Duong says, “Healthcare workers and essential workers need to go to work during the Covid-19 pandemic. That puts them in danger even if they are wearing masks and gloves. They should be equipped with more information about their own health and the likelihood of infection in their places of work to make informed decisions. If they are empowered with a wearable that can alert them of the imminent infection of Covid-19 or if the probability of infection is extremely high, they can immediately decide to isolate themselves from their families, etc.”

Complex Adaptive Systems can potentially help us find a “Covid-19 data signature” from observations made from interaction data collected inside the population of people who are infected and who are not infected.

By discovering patterns using Artificial Intelligence and causal inference, conceptual groups can be identified and data can be analyzed in the context of what is happening within the society.

The media does a good job of scrutinizing AI systems for privacy, data ownership and security issues. It is possible to build a Complex Adaptive System that gives individuals their data ownership, preserves privacy and is secure. At the same time, through Artificial Intelligence with causal inference, a decision network can be created to inform policymakers. Much like the Markov Decision Process, data can be modeled in simulations. A percentage of the population can wear the wearables so that the least amount of data necessary for accurate decisioning can be collected and used for policy.

The original article can be found here.

It is useful to note that AI with causal inference has been developed by professor Judea Pearl, who is awarded Turing Award 2011, the most prestigious prize awarded to computer scientists by the Association for Computing Machinery. In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Judea Pearl also contribute on Causal Inference for AI transparency, which is one of important AIWS.net topics on AI Ethics.