TY - CHAP A1 - Zeller, Marc A1 - Waschulzik, Thomas A1 - Carlan, Carmen A1 - Serahlazau, Marat A1 - Bahlmann, Claus A1 - Wu, Zhiliang A1 - Spieckermann, Sigurd A1 - Krompass, Denis A1 - Geerkens, Simon A1 - Sieberichs, Christian A1 - Kirchheim, Konstantin A1 - Özen, Batu Kaan A1 - Robles, Lucia Diez ED - Ceccarelli, Andrea ED - Trapp, Mario ED - Bondavalli, Andrea ED - Schoitsch, Erwin ED - Gallina, Barbara ED - Bitsch, Friedemann T1 - Continuous Development and Safety Assurance Pipeline for ML-Based Systems in the Railway Domain T2 - Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops. DECSoS, SASSUR, TOASTS, and WAISE, Florence, Italy, September 17, 2024, Proceedings. Lecture Notes in Computer Science, vol 14989 KW - Maschinelles Lernen KW - Sicherheit KW - Eisenbahn KW - Autonomes Fahrzeug Y1 - 2024 SN - 9783031687372 U6 - https://doi.org/10.1007/978-3-031-68738-9_36 SN - 0302-9743 SP - 446 EP - 459 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Geerkens, Simon A1 - Sieberichs, Christian A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - QI²: an interactive tool for data quality assurance JF - AI and Ethics N2 - The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also, the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper, we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well-known MNIST data set based an handwritten digits. KW - Performance metrics KW - HSD Publikationsfonds KW - DEAL KW - DFG Publikationskosten KW - Machine learning KW - Quality assurance KW - Data integrity KW - Data quality Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-42945 SN - 2730-5961 N1 - Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences VL - 4 SP - 141 EP - 149 PB - Springer Nature ER - TY - JOUR A1 - Sieberichs, Christian A1 - Geerkens, Simon A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - ECS: an interactive tool for data quality assurance JF - AI and Ethics N2 - With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper, we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems. KW - DEAL KW - HSD Publikationsfonds KW - Data visulization KW - Distance based KW - Data quality assurance KW - Equivalence class sets KW - DFG Publikationskosten Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-42908 SN - 2730-5961 N1 - Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences VL - 4 SP - 131 EP - 139 PB - Springer Nature ER - TY - INPR A1 - Geerkens, Simon A1 - Sieberichs, Christian A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - QI2 -- an Interactive Tool for Data Quality Assurance N2 - The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well known MNIST data set based an handwritten digits. KW - Data Structures and Algorithms KW - Machine Learning KW - Computers and Society KW - Artificial Intelligence Y1 - 2023 PB - arXiv ER - TY - INPR A1 - Sieberichs, Christian A1 - Geerkens, Simon A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - ECS -- an Interactive Tool for Data Quality Assurance N2 - With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems. KW - Artificial Intelligence KW - Machine Learning KW - PrePrint KW - Systems and Control Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2307.04368 PB - arXiv ER -