Maintaining the main aspects of the algebraic specification language ASF as presented in [Bergstra&al.89] we have extend ASF with the following concepts: While once exported names in ASF must stay visible up to the top the module hierarchy, ASF+ permits a more sophisticated hiding of signature names. The erroneous merging of distinct structures that occurs when importing different actualizations of the same parameterized module in ASF is avoided in ASF+ by a more adequate form of parameter binding. The new ``Namensraum''-concept of ASF+ permits the specifier on the one hand directly to identify the origin of hidden names and on the other to decide whether an imported module is only to be accessed or whether an important property of it is to be modified. In the first case he can access one single globally provided version; in the second he has to import a copy of the module. Finally ASF+ permits semantic conditions on parameters and the specification of tasks for a theorem prover.
In many practical tasks it is needed to estimate an effect of treatment on individual level. For example, in medicine it is essential to determine the patients that would benefit from a certain medicament. In marketing, knowing the persons that are likely to buy a new product would reduce the amount of spam. In this chapter, we review the methods to estimate an individual treatment effect from a randomized trial, i.e., an experiment when a part of individuals receives a new treatment, while the others do not. Finally, it is shown that new efficient methods are needed in this domain.
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.
This is a short (and personal) introduction in German to the connections between artificial intelligence, philosophy, and logic, and to the author's work. Dies ist eine kurze (und persoenliche) Einfuehrung in die Zusammenhaenge zwischen Kuenstlicher Intelligenz, Philosophie, und Logik, und in die Arbeiten des Autors.