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Auflistung nach Autor:in "Braun, Daniel"

1 - 4 von 4
  • Konferenzbeitrag
    Consumer Protection in the Digital Era: The Potential of Customer-Centered LegalTech
    (INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, 2019) Braun, Daniel; Scepankova, Elena; Holl, Patrick; Matthes, Florian
    New technologies and tools, often summarised under the term “LegalTech”, are changing the way in which legal professionals work. The digital transformation has changed many aspects of our daily life and democratised access to knowledge and services. In the legal domain, however, consumers rarely benefit from digitisation. On the contrary, they are often overpowered by big corporations and their well equipped legal departments. In this paper, we outline how LegalTech can be used to empower consumers in the digital era, by building tools to support consumers and those who protect them. In order to show the potential of customer-centered LegalTech, we present two prototypes which semantically analyse, assess, and summarise Terms of Services from German web shops.
  • Konferenzbeitrag
    Detection and Implicit Classification of Outliers via Different Feature Sets in Polygonal Chains
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017) Singhof, Michael; Klassen, Gerhard; Braun, Daniel; Conrad, Stefan
    Many outlier detection tasks involve a classification of outliers of di erent types. Most standard procedures solve this problem in two steps: First, an outlier detection algorithm is carried out, which is normally trained on outlier free data, only, since the samples of outliers are limited. Second, the outliers detected in that step, are classified with a conventional classification algorithm, that needs samples for all classes. However, often the quality of the classification is lowered due to the small number of available samples. Therefore, in this work, we introduce an outlier detection and classification algorithm, that does not depend on training data for the classification process. Instead, we assume, that di erent kinds of outliers are inferred by di erent processes and as such should be detected by different outlier detection approaches. This work focuses on the example of outliers in mountain silhouettes.
  • Konferenzbeitrag
    Generating Explanations for Algorithmic Decisions of Usage-Based Insurances using Natural Language Generation
    (Software Engineering und Software Management 2018, 2018) Braun, Daniel; Matthes, Florian
    Usage-based insurances are becoming more and more popular, especially for cars. These so called telematics insurances use different sensors installed in a car to track the individual driving style of the driver. Instead of calculating insurance premiums based on statistical risk groups, insurance companies can use these data to create individual risk profiles and calculate insurance premiums accordingly. We present an approach to use Natural Language Generation (NLG) in order to explain customers which aspects of their behaviour influenced the assessment of the algorithm. In this way, we can not only increase the acceptance of customers regarding such systems, but also positively influence their future behaviour.
  • Conference Proceedings
    Informatik-Grundlagen für Lehramtsstudierende
    (INFOS 2021 – 19. GI-Fachtagung Informatik und Schule, 2021) Braun, Daniel; Pampel, Barbara; Seiss, Melanie