A Contribution to COVID-19 Prevention through Crowd Collaboration using Conversational AI & Social Platforms

Citation:

Haqbeen J, Ito T, Sahab S, Hadfi R, Okuhara S, Saba N, Hofiani M, Baregzai U. A Contribution to COVID-19 Prevention through Crowd Collaboration using Conversational AI & Social Platforms, in AI for Social Good Workshop. ; 2020.

Abstract:

COVID-19 Prevention, which combines the soft approaches and best practices for public health safety, is the only recommended solution from the health science and management society side considering the pandemic era. This process must be promoted via facilitation support to collective urban awareness programs through public dialogue and collective intelligence. Moreover, support must be provided throughout the process to perform complex public deliberation to find issues and ideas within existing approaches that can result in better approaches towards prevention. In an attempt to evaluate the validity of such claims in a conflict and COVID-19-affected country like Afghanistan, we conducted a large-scale digital social experiment using conversational AI and social platforms from an info-epidemiology and an info-veillance perspective. This served as a means to uncover an underling truth, give large-scale facilitation support, extend the soft impact of discussion to multiple sites, collect, diverge, converge and evaluate a large amount of opinions and concerns from health experts, patients and local people, deliberate on the data collected and explore collective prevention approaches of COVID-19. Finally, this paper shows that deciding a prevention measure that maximizes the probability of finding the ground truth is intrinsically difficult without utilizing the support of an AI-enabled discussion systems.

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