05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Institute: search.filters.UBSInstitut.Fakultätsübergreifend / Sonstige EinrichtungPublication Type: search.filters.UBSTyp.ZeitschriftenartikelAuthor: search.filters.author.Staab, Steffen

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    MBFair : a model-based verification methodology for detecting violations of individual fairness
    (2024) Ramadan, Qusai; Konersmann, Marco; Ahmadian, Amir Shayan; Jürjens, Jan; Staab, Steffen
    Decision-making systems are prone to discrimination against individuals with regard to protected characteristics such as gender and ethnicity. Detecting and explaining the discriminatory behavior of implemented software is difficult. To avoid the possibility of discrimination from the onset of software development, we propose a model-based methodology called MBFair that allows for verifying UML-based software designs with regard to individual fairness. The verification in MBFair is performed by generating temporal logic clauses, whose verification results enable reporting on the individual fairness of the targeted software. We study the applicability of MBFair using three case studies in real-world settings including a bank services system, a delivery system, and a loan system. We empirically evaluate the necessity of MBFair in a user study and compare it against a baseline scenario in which no modeling and tool support is offered. Our empirical evaluation indicates that analyzing the UML models manually produces unreliable results with a high chance of 46% that analysts overlook true-positive discrimination. We conclude that analysts require support for fairness-related analysis, such as our MBFair methodology.
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    Analyzing the influence of hyper-parameters and regularizers of topic modeling in terms of Renyi entropy
    (2020) Koltcov, Sergei; Ignatenko, Vera; Boukhers, Zeyd; Staab, Steffen
    Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models-Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)-we, first of all, show that the minimum of Renyi entropy coincides with the “true” number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.