How the Coronavirus Pandemic Is Breaking Artificial Intelligence and How to Fix It

Original article was published by on Communications of the ACM – Artificial Intelligence



By Gizmodo

July 30, 2020
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Artificial intelligence algorithms are prone to becoming unreliable when rare events like the Covid-19 pandemic happen.

Credit: Angelica Alzona/Gizmodo

As covid-19 disrupted the world in March, online retail giant Amazon struggled to respond to the sudden shift caused by the pandemic. Household items like bottled water and toilet paper, which never ran out of stock, suddenly became in short supply. One- and two-day deliveries were delayed for several days. Though Amazon CEO Jeff Bezos would go on to make $24 billion during the pandemic, initially, the company struggled with adjusting its logistics, transportation, supply chain, purchasing, and third-party seller processes to prioritize stocking and delivering higher-priority items.

Under normal circumstances, Amazon’s complicated logistics are mostly handled by artificial intelligence algorithms. Honed on billions of sales and deliveries, these systems accurately predict how much of each item will be sold, when to replenish stock at fulfillment centers, and how to bundle deliveries to minimize travel distances. But as the coronavirus pandemic crisis has changed our daily habits and life patterns, those predictions are no longer valid.

“In the CPG [consumer packaged goods] industry, the consumer buying patterns during this pandemic has shifted immensely,” Rajeev Sharma, SVP and global head of enterprise AI solutions & cognitive engineering at AI consultancy firm Pactera Edge, told Gizmodo. “There is a tendency of panic buying of items in larger quantities and of different sizes and quantities. The [AI] models may have never seen such spikes in the past and hence would give less accurate outputs.”

Among the many things the coronavirus outbreak has highlighted is how fragile our AI systems are. And as automation continues to become a bigger part of everything we do, we need new approaches to ensure our AI systems remain robust in face of black swan events that cause widespread disruptions.

Artificial intelligence algorithms are behind many changes to our daily lives in the past decades. They keep spam out of our inboxes and violent content off social media, with mixed results. They fight fraud and money laundering in banks. They help investors make trade decisions and, terrifyingly, assist recruiters in reviewing job applications. And they do all of this millions of times per day, with high efficiency—most of the time. But they are prone to becoming unreliable when rare events like the covid-19 pandemic happen.

Among the many things the coronavirus outbreak has highlighted is how fragile our AI systems are. And as automation continues to become a bigger part of everything we do, we need new approaches to ensure our AI systems remain robust in face of black swan events that cause widespread disruptions.

 

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