When the environment changes, as during the pandemic, many AI models start to break.

Judge as of May 2020. TRUE: reporting or research from around then documents many AI or machine learning models (several across different domains, such as demand forecasting, inventory, fraud detection, recommendation or credit models) degrading or failing because behaviour or data shifted during the COVID-19 pandemic, or more generally documents that distribution shift commonly breaks models. MOSTLY TRUE: documented breakdowns exist across several cases, but 'lots' somewhat overstates how widespread they were. MIXED: there are only a few documented cases alongside reports that many models coped. MOSTLY FALSE: only isolated anecdotes exist, presented as a general phenomenon. FALSE: evidence shows that models largely kept working through the pandemic's changes.

Set before the research began.

1. E1 The algorithms big companies use to manage their supply chains don’t work during pandemics The Verge · 27 April 2020

   ““That’s what we’re seeing over and over,” Beal says. “A lot of these systems haven’t caught up.””

2. E2 The algorithms big companies use to manage their supply chains don’t work during pandemics The Verge · 27 April 2020

   “The models can still be used. “It’s the data that you input that has to be changed,” Nagurney says.”

3. E3 What to Do Now That Your Demand Forecast Is Wrong Bain & Company · 7 April 2020

   “Due to the crisis, most demand forecast models in place today are no longer accurate.”

4. E4 Concept Drift and the Impact of COVID-19 on Data Science Iguazio · 30 April 2020

   “Concept drift is also impacting models designed to predict fraud across various industries. For example, previously models were trained to view the purchase of one-way flight tickets as a strong indicator of airline fraud.”

Bain's April 2020 analysis says most demand forecast models in use were no longer accurate because of the crisis (E3). The Verge quotes Beal saying "That's what we're seeing over and over" and that "a lot of these systems haven't caught up" in an article on supply-chain algorithms during the pandemic (E1). Together these show model degradation in several domains around May 2020. E2 notes the models could still be used once the input data changed, but that describes how to fix them rather than showing they kept working.