  This master's thesis discusses an important issue regarding how algorithmic
decision making (ADM) is used in crime forecasting. In America forecasting
tools are widely used by judiciary systems for making decisions about risk
offenders based on criminal justice for risk offenders. By making use of such
tools, the judiciary relies on ADM in order to make error free judgement on
offenders. For this purpose, one of the quality measures for machine learning
techniques which is widly used, the $AUC$ (area under curve), is compared to
and contrasted for results with the $PPV_k$ (positive predictive value).
Keeping in view the criticality of judgement along with a high dependency on
tools offering ADM, it is necessary to evaluate risk tools that aid in decision
making based on algorithms. In this methodology, such an evaluation is
conducted by implementing a common machine learning approach called binary
classifier, as it determines the binary outcome of the underlying juristic
question. 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 the
deviation can rise up to 0.75. In order to test this deviation on an already in
used Classifier, data from the fourth generation risk assement tool COMPAS was
used. The result were were quite alarming as the two measures derivate from
each other by 0.48. In this study, the risk assessment evaluation of the
forecasting tools was successfully conducted, carefully reviewed and examined.
Additionally, it is also discussed whether such systems used for the purpose of
making decisions should be socially accepted or not.
