Machine learning and AI-based tools used in response to COVID-19 are likely to improve some human activities and provide essential information needed to make certain personal or professional decisions; however, they also highlight some pervasive challenges faced by both machines and the humans who create them.
However, the observed advances in AI / machine learning that led to and during the COVID-19 pandemic cannot be ignored. This global economic and health crisis brings with it a unique opportunity for modeling upgrades and innovation, provided certain basic principles are followed.
Here are four industry truths (note: this is not an exhaustive list) my colleagues and I have found that the matter is in any design climate, but particularly during a climate of global pandemic.
Some success can be attributed to chance rather than reasoning
When a large group of people collectively work on a problem, success can become more likely. Looking at historical examples such as the 2008 global financial crisis, there were several analysts credited with predicting the crisis. This may seem miraculous to some until it is considered that more than 200,000 people worked on Wall Street, each making their own predictions. So it becomes less of a miracle and more of a statistically probable outcome. With so many people working on models and predictions simultaneously, it was very likely that someone would have figured it out by accident.
Likewise, with COVID-19, there are many people involved, from statistical modelers and data scientists to vaccine specialists, and there is also an incredible eagerness to find concrete data-driven solutions and answers. By following appropriate statistical rigor, coupled with machine learning and artificial intelligence, it is possible to improve these models and reduce the chances of false predictions resulting from too many predictions.
Automation can help maintain productivity when used wisely
During a crisis, time management is essential. Automation technology can be used not only as part of the crisis solution, but also as a tool to monitor the productivity and contribution of team members working on the solution. For modeling, automation can also greatly improve the speed of results. Every second a piece of software can do automation for a model, it allows a data scientist (or even a medical scientist) to do other, more important tasks. The user-friendly platforms on the market now offer more people, such as business analysts, access to forecasts from custom machine learning models.
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