by the public and affected parties which may be a violation of due process. Additionally, simple, transparent and more interpretable algorithms (such as linear regression) have been shown to perform predictions approximately as well as the COMPAS algorithm. Another general criticism of machine-learning based algorithms is since they are data-dependent if the data are biased, the software will likely yield biased results. Specifically, COMPAS risk assessments have been argued to violate 14th Amendment Equal Protection rights on the basis of race, since the algorithms are argued to be racially discriminatory, to result in disparate treatment, and to not be narrowly tailored. Accuracy In 2016, Julia Angwin was co-author of a ProPublica investigation of the algorithm. The team found that “blacks are almost twice as likely as whites to be labeled a higher risk but not actually re-offend,” whereas COMPAS “makes the opposite mistake among whites: They are much more likely than blacks to be labeled lower-risk but go on to commit other crimes.” They also found that only 20 percent of people predicted to commit violent crimes actually went on to do so. In a letter, Northpointe criticized ProPublica’s methodology and stated that: “[The company] does not agree that the results of your analysis, or the claims being made based upon that analysis, are correct or that they accurately reflect the outcomes from the application of the model.” Another team at the Community Resources for Justice, a criminal justice think tank, published a rebuttal of the investigation's findings. Among several objections, the CRJ rebuttal concluded that the Propublica's results: "contradict several comprehensive existing studies concluding that actuarial risk can be predicted free of racial and/or gender bias." A subsequent study has shown that COMPAS software is somewhat more accurate than individuals with little or no criminal justice expertise, yet less accurate than groups of such individuals. They found that: "On average, they got the right answer 63 percent of their time, and the group’s accuracy rose to 67 percent if their answers were pooled. COMPAS, by contrast, has an accuracy of 65 percent." Further reading Sample COMPAS Risk Assessment See also Algorithmic bias Garbage in, garbage out Legal expert systems Loomis v. Wisconsin Criminal sentencing in the United States References Criminal justice in the United States Government by algorithm Legal software