In an article published yesterday on WIRED.com, writer Issie Lapowsky reported on research at Dartmouth College which found that untrained online workers predicted recidivism about as accurately as Compas, one of the most popular crime-prediction algorithms available. Penn Criminology professor, Richard Berk, a leader in machine learning and creator of the algorithm used by Philadelphia Adult Probation and Parole Department, commented on these findings. He brought up the important policy decisions that create the foundation for any such algorithm: "The question is: What are the different kinds of unfairness? How does the model perform for each of them?" he says. "There are tradeoffs between them, and you cannot evaluate the fairness of an instrument unless you consider all of them."