Sympathy for the Learner post-COVID

One thing is certain, COVID-19 has upended traditional relationships within our data. Even if the economy returns to a more normal rhythm how we interact with others, react to stimuli and even our life long aspirations, have changed forever. We will adjust and find a new normal, just as humanity has in times of crisis and great tragedy before, but we must recognize, this change will also have an impact on our new workplace partners, Artificial Intelligence (AI).  And now that AI is becoming a ubiquitous part of everyone’s daily lives, when your AI gets confused, it may do real-world harm on an already struggling populace. 

As we adjust and learn how to navigate this new world we have access to a well-educated brain, creativity, and intuition that AI does not have. The world they see, the topology of their training data, has shifted drastically to show unfamiliar terrains and broken fundamental relationships. For example, what served as an incentive to human behavior (a crowded beach) might now cause fear in a large segment of the population. This is a time to make sure you continuously measure your AI’s performance, focus your data scientist on understanding the impact of the COVID-19, and be open-minded to a variety of AI methods. You must have sympathy for your learner (AI) but you should also make sure the right one is being used to answer your questions and to solve your problems. 

Post Covid-19, sympathy for the learner is not just required for high-quality models but necessary to assure minimum performing models. The world has changed, quite dramatically. The learner you choose (or how you present the data to the learner) needs to be able to adjust rapidly to this ever-changing landscape in production. Severe concept drift, when fundamental drivers to target relationships change, is now the expected with all models. Key drivers of our economy, behavior, and relationships are rapidly evolving but our historical data still see the old world order. Even something as simple as toilet paper purchases is now distorted. A sudden peak in toilet paper purchases for a given household prior to COVID-19 might mean a particularly good sale, more people moving into the household, or purchases for a small business. Now it probably means run of the mill hoarding. Recommendation engines, risk models and segmentation scores will collapse as this new data enters the system. Worse still, as the data distorted by the shelter in place order enters the training data the AI will try to use it to project into the future rather than understand this was a transitory event. Care must be taken when presenting this data to your learner. The learner has not been following the news, does not understand the concept of a global pandemic, and has no ability to imagine the future post Covid19. While this is frustrating, as long as you recognize the limitations of AI you can mitigate the impact of Covid19 on your modeling data and, again, get back to looking into the future with AI.

 

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