Home → Magazine Archive → November 2019 (Vol. 62, No. 11) → The Effects of Mixing Machine Learning and Human Judgment → Abstract

The Effects of Mixing Machine Learning and Human Judgment

By Michelle Vaccaro, Jim Waldo

Communications of the ACM, Vol. 62 No. 11, Pages 104-110
10.1145/3359338

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In 1997, IBM's Deep Blue software beat the World Chess Champion Garry Kasparov in a series of six matches. Since then, other programs have beaten human players in games ranging from "Jeopardy!" to Go. Inspired by his loss, Kasparov decided in 2005 to test the success of Human+AI pairs in an online chess tournament.2 He found the Human+AI team bested the solo human. More surprisingly, he also found the Human+AI team bested the solo computer, even though the machine outperformed humans.

Researchers explain this phenomenon by emphasizing that humans and machines excel in different dimensions of intelligence.9 Human chess players do well with long-term chess strategies, but they perform poorly at assessing the millions of possible configurations of pieces. The opposite holds for machines. Because of these differences, combining human and machine intelligence produces better outcomes than when each works separately. People also view this form of collaboration between humans and machines as a possible way to mitigate the problems of bias in machine learning, a problem that has taken center stage in recent months.12

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