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This master's thesis discusses an important issue regarding how algorithmicdecision making (ADM) is used in crime forecasting. In America forecastingtools are widely used by judiciary systems for making decisions about riskoffenders based on criminal justice for risk offenders. By making use of suchtools, the judiciary relies on ADM in order to make error free judgement onoffenders. For this purpose, one of the quality measures for machine learningtechniques which is widly used, the $AUC$ (area under curve), is compared toand contrasted for results with the $PPV_k$ (positive predictive value).Keeping in view the criticality of judgement along with a high dependency ontools offering ADM, it is necessary to evaluate risk tools that aid in decisionmaking based on algorithms. In this methodology, such an evaluation isconducted by implementing a common machine learning approach called binaryclassifier, as it determines the binary outcome of the underlying juristicquestion. This thesis showed that the $PPV_k$ (positive predictive value)technique models the decision of judges much better than the $AUC$. Therefore,this research has investigated whether there exists a classifier for which the$PPV_k$ deviates from $AUC$ by a large proportion. It could be shown that thedeviation can rise up to 0.75. In order to test this deviation on an already inused Classifier, data from the fourth generation risk assement tool COMPAS wasused. The result were were quite alarming as the two measures derivate fromeach other by 0.48. In this study, the risk assessment evaluation of theforecasting tools was successfully conducted, carefully reviewed and examined.Additionally, it is also discussed whether such systems used for the purpose ofmaking decisions should be socially accepted or not.

Qualit\"atsma{\ss}e bin\"arer Klassifikationen im Bereich kriminalprognostischer Instrumente der vierten Generation