Tobias D. Krafft

Marc P. Hauer, Tobias D Krafft, Andreas Sesing-Wagenpfeil et al.

With the Proposal for a Regulation laying down harmonised rules on Artificial Intelligence (AI Act) the European Union provides the first regulatory document that applies to the entire complex of AI systems. While some fear that the regulation leaves too much room for interpretation and thus bring little benefit to society, others expect that the regulation is too restrictive and, thus, blocks progress and innovation, as well as hinders the economic success of companies within the EU. Without a systematic approach, it is difficult to assess how it will actually impact the AI landscape. In this paper, we suggest a systematic approach that we applied on the initial draft of the AI Act that has been released in April 2021. We went through several iterations of compiling the list of AI products and projects in and from Germany, which the Lernende Systeme platform lists, and then classified them according to the AI Act together with experts from the fields of computer science and law. Our study shows a need for more concrete formulation, since for some provisions it is often unclear whether they are applicable in a specific case or not. Apart from that, it turns out that only about 30\% of the AI systems considered would be regulated by the AI Act, the rest would be classified as low-risk. However, as the database is not representative, the results only provide a first assessment. The process presented can be applied to any collections, and also repeated when regulations are about to change. This allows fears of over- or under-regulation to be investigated before the regulations comes into effect.

Tobias D. Krafft

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.