Artificial intelligence (AI) deep learning models can help businesses set optimal prices for goods or service by extrapolating prices from historical sales data, which generally show that sales go down as prices go up. But these predictions become unreliable when circumstances differ from the time the source data was generated, such as when the COVID-19 pandemic disrupted manufacturing supply chains and drastically altered consumer demands.

UC Riverside School of Business scholars and their collaborators solved this problem by developing a deep learning model that considers both historical sales data and the economic theory of demand. Economic theory of demand accounts for factors such as income levels, consumer preferences, and consumption patterns under various circumstances such as holidays or extreme events like pandemics and natural disasters. 

Researchers from Brazilian, Argentine, and Uruguayan institutions analyze the barriers that low- and middle-income countries face in disseminating research on intensive care medicine, particularly in the treatment of critically ill patients. Published this month in The Lancet, the study highlights how historical and economic biases perpetuate inequalities and suggests changes to make the scientific publishing system more inclusive and representative of the global community.