TY - RPRT A1 - Jackszis, Arabella T1 - Diskriminierung im Machine Learning und Erklärbarkeit von Algorithmen T1 - Discrimination in machine learning and explainability of algorithms N2 - Künstliche Intelligenz und Machine Learning sind hochaktuelle Themen in der Informatik. Fortschrittliche Technologien und Vorgehensweisen schaffen neue Einsatzfelder für Anwendungen, die Verhaltensweisen des Menschen erlernen und nachahmen. Dieses innovative Vorgehen birgt jedoch einige Gefahren. Das menschliche Verhalten ist nicht immer objektiv und korrekt. Oft beherrschen Vorurteile das Treffen menschlicher Entscheidungen, sodass Personen diskriminiert werden. Es besteht das Risiko, dass Maschinen diese Verhaltensweise übernehmen. Hinzu kommt die fehlende Erklärbarkeit der Algorithmen. Je nach Modell ist nicht eindeutig, nach welchen Prinzipien eine Entscheidung getroffen wird. Das erschwert das Aufdecken von Diskriminierung und senkt die Vertrauenswürdigkeit des Systems. In dieser Arbeit werden Ursachen für Diskriminierung und fehlende Transparenz, sowie Lösungsmethoden erarbeitet. Dabei wird erörtert, inwieweit die beiden Themen aufeinander einspielen und gemeinsam zur Qualität der Software beitragen. N2 - Artificial Intelligence and Machine Learning are matters of high interest in Information Technology. Advanced technologies and approaches are creating new areas of use for applications that study and imitate human behavior. At the same time, this innovative approach poses new dangers. Human behavior is not always objective and correct. Prejudice often dominates human decisions and therefore leads to discrimination. There is a risk that machines adopt this behavior. On top of this, algorithms are often not explainable. Depending on the model, the principles that are relevant for the decision are not clear. This makes it harder to detect discrimination and reduces the trustworthiness of a system. In this paper, causes of bias in Machine Learning and the lack of transparency, as well as methods to eliminate these problems, are elaborated. It works out the interaction between the two issues and how they contribute to the quality of the software. KW - Diskriminierung KW - Discrimination KW - Fairness KW - Erklärbarkeit KW - Nachvollziehbarkeit KW - Transparenz KW - Machine Learning KW - explainability KW - transparency KW - artificial intelligence KW - machine learning KW - traceability KW - Künstliche Intelligenz KW - Hg_TCR Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-25075 VL - Arbeitspapier des Lehrgebiets Datenbanken und E-Business, Nr. 2/2021 CY - Düsseldorf ER - TY - JOUR A1 - Greb, Fabian A1 - Steffens, Jochen A1 - Schlotz, Wolff T1 - Modeling Music-Selection Behavior in Everyday Life: A Multilevel Statistical Learning Approach and Mediation Analysis of Experience Sampling Data JF - Frontiers in psychology N2 - Music listening has become a highly individualized activity with smartphones and music streaming services providing listeners with absolute freedom to listen to any kind of music in any situation. Until now, little has been written about the processes underlying the selection of music in daily life. The present study aimed to disentangle some of the complex processes among the listener, situation, and functions of music listening involved in music selection. Utilizing the experience sampling method, data were collected from 119 participants using a smartphone application. For 10 consecutive days, participants received 14 prompts using stratified-random sampling throughout the day and reported on their music-listening behavior. Statistical learning procedures on multilevel regression models and multilevel structural equation modeling were used to determine the most important predictors and analyze mediation processes between person, situation, functions of listening, and music selection. Results revealed that the features of music selected in daily life were predominantly determined by situational characteristics, whereas consistent individual differences were of minor importance. Functions of music listening were found to act as a mediator between characteristics of the situation and music-selection behavior. We further observed several significant random effects, which indicated that individuals differed in how situational variables affected their music selection behavior. Our findings suggest a need to shift the focus of music-listening research from individual differences to situational influences, including potential person-situation interactions. KW - music-listening behavior KW - music-selection behavior KW - machine learning Y1 - 2019 U6 - https://doi.org/10.3389/fpsyg.2019.00390 N1 - This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). VL - 10 PB - Frontiers ER -