In Scenario Planning, AI and Covid-19 will challenge assumptions about what factors shape our future.
Even for those who developed scenarios in which the Covid-19 outbreak occurred, that it has occurred means those scenarios collapse into a set of certainties. The surrounding scenarios no longer hold true. The number of uncertainties currently outstrips scenario planning’s ability to help people imagine futures. In the midst of a future unfolding, alternative futures become at best arbitrary. The pace of change is too fast for the process to keep up.
But now is the time to start thinking about putting labels on the uncertainties that will emerge as robust after the Covid-19 threat wanes.
One of those uncertainties is likely to be AI-ML, at least its machine learning incarnation.
Covid-19 will create vast amounts of data. That data will contain patterns about how to identify illness, how to anticipate hotspots and perhaps even ways to curtail the virus’s virulence.
The effectiveness of AI-ML to assist in solving the Covid-19 conundrum will likely prove a defining uncertainty during the recovery period and beyond.
If AI-ML effectively helps find a way out of the crisis, AI will have secured its future as a technology worthy of ongoing investment. Although AI-ML remains limited by its data and constrained by narrow, mostly domain-specific models, it will have proven that with enough data it can find meaningful patterns that change outcomes, save lives and increase the speed of analysis significantly.
If on the other hand, AI comes up with a number of isolated insights that don’t add up to enough for it to play a major factor in the event, then AI-ML may well exit the Covid-19 crisis as a hyped technology that did not fulfill its promise.
No uncertainty, however, exists in a vacuum. A lack of trust by practitioners uncertain about AI-ML’s effectiveness plays against the trust of AI-ML output, which defines a social uncertainty.
AI is already being touted for a number of areas, from monitoring the potentially infected for signs of disease emergence (How Baidu is bringing AI to the fight against coronavirus, MIT Review), to sorting lung scans and identifying Covid-19-based pneumonia from other lung diseases (AI algorithm IDs abnormal chest X-rays from COVID-19 patients, Healthcare IT News), to analyzing the media for public health response lessons learned (McGill professors part of federally-funded Covid-19 research initiative, McGill Reporter).
The trust of AI-ML and AI-ML’s ultimate effectiveness during the Covid-19 outbreak will likely both remain on the list of uncertain and important factors that will shape our future.
A key next stage uncertainty: What will be the economic model Post-Covid-19.
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