The Effects of Mixing Machine Learning and Human Judgment

The Effects of Mixing Machine Learning and Human Judgment

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 that the Human+AI team bested the solo human. More surprisingly, he also found that the Human+AI team bested the solo computer, even though the machine outperformed humans. We decided to investigate this type of collaboration between humans and machines using risk-assessment algorithms as a case study.

In particular, we looked at the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm, a well-known (perhaps infamous) risk-prediction system, and its effect on human decisions about risk. Many state courts use algorithms such as COMPAS to predict defendants’ risk of recidivism, and these results inform bail, sentencing, and parole decisions. Prior work on risk-assessment algorithms has focused on their accuracy and fairness, but it has not addressed their interactions with human decision makers who serve as the final arbitrators.

In one study from 2018, Julia Dressel and Hany Farid compared risk assessments from the COMPAS software and Amazon Mechanical Turk workers, and found that the algorithm and the humans achieved similar levels of accuracy and fairness.6 This study signals an important shift in the literature on risk-assessment instruments by incorporating human subjects to contextualize the accuracy and fairness of the algorithms. Dressel and Farid’s study, however, divorces the human decision makers and the algorithm when, in fact, the current model indicates that humans and algorithms would work in tandem.