RECENT PUBLICATION: Article
Making Sense of Recommendations
Computer algorithms are increasingly being used to predict people's preferences and make recommendations. Although people frequently encounter these algorithms because they are cheap to scale, we do not know how they compare to human judgment. Here, we compare computer recommender systems to human recommenders in a domain that affords humans many advantages: predicting which jokes people will find funny. We find that recommender systems outperform humans, whether strangers, friends, or family. Yet people are averse to relying on these recommender systems. This aversion partly stems from the fact that people believe the human recommendation process is easier to understand. It is not enough for recommender systems to be accurate, they must also be understood.
Keywords: recommender systems; artificial intelligence; interpretability; Information Technology; Forecasting and Prediction; Decision Making; Attitudes;
Citation:
Yeomans, Michael, Anuj Shah, Sendhil Mullainathan, and Jon Kleinberg. "Making Sense of Recommendations." Journal of Behavioral Decision Making 32, no. 4 (October 2019): 403–414. View Details
