more than one-third of banks reported that their predictive algorithms became more inaccurate during the pandemicHow is it that COVID infected our algorithms? The answers are subtle, but offer important lessons since the COVID era will likely impact algorithms for years to come.First, algorithms do best at pattern recognition. They are usually designed using years of historical data to predict outcomes in the future. However, nearly every input into AI algorithms changed during COVID.
Third, COVID’s impact on health care and spending habits were particularly stark for marginalized populations, and that has led to algorithms being more likely to misfire for poor and nonwhite individuals. Prior to COVID, nonwhite and low-income Americans were significantly more likely to pay cash in a store rather than shop online. Fast-forward to the pandemic, where all segments of the U.S. population shifted from brick-and-mortar stores to online purchasing.
First, humans should exercise greater oversight over AI algorithms—at least for the time being. Any organization that uses pre-COVID AI algorithms should double-check their performance, particularly for how they are affecting marginalized groups like Black Americans and other minorities. Second, if these checks reveal any red flags, organizations should redevelop their algorithms using data from the pandemic era. This is particularly relevant for algorithms that use inputs that are still affected by COVID.
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