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PublicationTowards an ELSA Curriculum for Data Scientists( 2024-04-11)The use of artificial intelligence (AI) applications in a growing number of domains in recent years has put into focus the ethical, legal, and societal aspects (ELSA) of these technologies and the relevant challenges they pose. In this paper, we propose an ELSA curriculum for data scientists aiming to raise awareness about ELSA challenges in their work, provide them with a common language with the relevant domain experts in order to cooperate to find appropriate solutions, and finally, incorporate ELSA in the data science workflow. ELSA should not be seen as an impediment or a superfluous artefact but rather as an integral part of the Data Science Project Lifecycle. The proposed curriculum uses the CRISP-DM (CRoss-Industry Standard Process for Data Mining) model as a backbone to define a vertical partition expressed in modules corresponding to the CRISP-DM phases. The horizontal partition includes knowledge units belonging to three strands that run through the phases, namely ethical and societal, legal and technical rendering knowledge units (KUs). In addition to the detailed description of the aforementioned KUs, we also discuss their implementation, issues such as duration, form, and evaluation of participants, as well as the variance of the knowledge level and needs of the target audience.
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PublicationManaging artificial intelligence applications in healthcare: Promoting information processing among stakeholders( 2024-04)AI applications hold great potential for improving healthcare. However, successfully operating AI is a complex endeavor requiring organizations to establish adequate management approaches. Managing AI applications requires functioning information exchange between a diverse set of stakeholders. Lacking information processing among stakeholders increases task uncertainty, hampering the operation of AI applications. Existing research lacks an understanding of holistic AI management approaches. To shed light on AI management in healthcare, we conducted a multi-perspective literature analysis followed by an interview study. Based on the organizational information processing theory, this paper investigates AI management in healthcare from an organizational perspective. As a result, we develop the AI application management model (AIAMA) that illustrates the managerial factors of AI management in healthcare and its interrelations. Furthermore, we provide managerial practices that improve information processing among stakeholders. We contribute to the academic discourse by providing a conceptual framework that increases the theoretical understanding of AI's management factors and understanding of management interrelations. Moreover, we contribute to practice by providing management practices that promote information processing and decrease task uncertainty when managing AI applications in healthcare.
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PublicationIdentification of Medical Ecosystems in the Field of Mental Health and Cardiovascular Diseases at the Cologne Site( 2024-03-15)As part of the Europe-wide smart health innovation hub implemented in the context of the Horizon Europe SHIFT-HUB project, our work concerns the identification of specific medical research ecosystems in the two fields, namely cardiovascular diseases and mental illness, with Cologne as the central location. To achieve this aim, the websites of involved organizations were used for data research purposes, and the members of each respective ecosystem or network were identified by acquiring information about their cooperation partners. A variety of selection criteria have been applied to filter out whether these partners were suitable to be considered as a further starting point for the research. The results indicate the existence of ecosystems in the two fields, with Cologne as the central location, in which various stakeholders, including healthcare institutions, healthcare providers, foundations, NGOs, and the business community, work closely together. Larger institutions are usually networked at an international level, while smaller institutions increasingly depend on and foster regional partnerships. This promotes cooperation and the exchange of knowledge at the regional level and facilitates direct contact with the people affected, i.e., patients' groups. Research institutions in both fields often receive financial support from commercial organizations, which highlights the importance of the business community's involvement in exploiting research results and promoting the quality of healthcare. The article highlights the complexity and interdisciplinarity of the particular ecosystems, with all the different categories of institutions comprising an indispensable position. The interaction amongst stakeholders at international, regional, and local levels can significantly help to deploy resources more effectively and improve the quality of life of people suffering from any of the two conditions.
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PublicationConsumer-centric electricity markets: Six design principles( 2024-03)Key to the necessary decarbonization of energy systems is the large-scale expansion of renewable energy sources and their integration into the electricity system. This integration is challenging because the feed-in from renewable energy sources is highly intermittent and largely dependent on uncontrollable factors such as weather patterns. To maintain grid stability, which refers to the required balance between demand and supply in the electricity system, flexibility is key. Large flexibility potentials can be found on the electricity demand side. However, current electricity market design in Europe, while providing major flexibility incentives, often neglects small-scale electricity consumers and distributed energy resources. We contribute to shape future electricity markets with consumers at the heart by developing six design principles for a consumer-centric electricity market design. We proceed by conducting a systematic literature review and evaluate the findings by expert interviews. Based on the developed design principles, we define a consumer-centric electricity market design as a set of market rules that align with the rules of other relevant energy markets and allow for the efficient matching of electricity demand and supply, with consumers having nondiscriminatory market access, being exposed to fine-grained price signals, being able to express their preferences, and having sufficient possibilities to protect themselves against unexpected price spikes. By actively incorporating consumers into electricity markets, we contribute to the overarching goal of integrating renewable energy sources while promoting energy justice, i.e., supporting a balanced mix of economic, political, environmental, and social interests.
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PublicationFake it till you make it: Synthetic data for emerging carsharing programs( 2024)Carsharing is an integral part of the transformation toward flexible and sustainable mobility. New carsharing programs are entering the market to challenge large operators by offering innovative services. This study investigates the use of generative machine learning models for creating synthetic data to support carsharing decision–making when data access is limited. To this end, it explores the evaluation, selection, and implementation of leading-edge methods, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), to generate synthetic tabular transaction data of carsharing trips. The study analyzes usage data of an emerging carsharing program that is expanding its services to include free-floating electric vehicles (EVs). The results show that augmenting real training data with synthetic samples improves predictive modeling of upcoming trips by up to 4.63%. These results support carsharing researchers and practitioners in generating and leveraging synthetic mobility data to develop solutions to real-world decision support problems in carsharing.
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PublicationDer Einfluss von digitalen Technologien auf Wissensarbeit: Kompetenzen im Wandel( 2024)
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PublicationSmartphone-Enabled Predictive Maintenance - Development and Implementation of a Reference Architecture and Processes( 2024)Predictive maintenance (PdM) is a hot topic in the field of manufacturing. However, its industry-wide realization lacks accepted integration concepts. Small and medium-sized enterprises (SMEs), in particular, tend to postpone PdM initiatives, primarily due to the high costs and effort of creating interoperability with established as well as in-use machines. PdM requires machine data to be proactively maintained. Therefore, in-use machines without integrated sensors must be replaced or cost-intensively upgraded. Furthermore, it is not advisable to invest in upgrades of existing machines, as they are cost-intensive, and their remaining lifespan is unknown as well as difficult to predict. One promising approach to applying PdM to these kinds of machines is the use of retail smartphones. With up to 16 sensors onboard, they offer an opportunity to cost-effectively collect required data without being tied to a single machine. Following a design science research approach, we present a reference software architecture consisting of a mobile and server application and reference processes for smartphone-enabled PdM to provide a lightweight approach, especially for SMEs. Together with five manufacturers and a software developer, we demonstrated and evaluated our artifacts using the software prototypes in a real-world setting.
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PublicationSkeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks( 2024)Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with the popularity of deep learning techniques. Furthermore, we see skeleton GTs used not only for training skeleton detectors with Convolutional Neural Networks (CNN), but also for evaluating skeleton-related pruning and matching algorithms. However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards. As a result, it is difficult to evaluate and reproduce CNN-based skeleton detectors and algorithms on a fair basis. In this paper, we present a heuristic strategy for object skeleton GT extraction in binary shapes and natural images. Our strategy is built on an extended theory of diagnosticity hypothesis, which enables encoding human-in-the-loop GT extraction based on clues from the target's context, simplicity, and completeness. Using this strategy, we developed a tool, SkeView, to generate skeleton GT of 17 existing shape and image datasets. The GTs are then structurally evaluated with representative methods to build viable baselines for fair comparisons. Experiments demonstrate that GTs generated by our strategy yield promising quality with respect to standard consistency, and also provide a balance between simplicity and completeness.
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PublicationErfolgsrezepte( 2024)Dieses Whitepaper wurde durch das Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie im Rahmen des "Fraunhofer Blockchain Center (20-3066-2-6-14)" gefördert. Wir danken an dieser Stelle für die Unterstützung.
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PublicationVergleich zweier Ansätze zur Bekämpfung der kalten Progression: Tarifverschiebung vs. einkommensteuerpflichtige Kopfpauschale( 2024)Die Bundesregierung hat im Jahr 2022 das Inflationsausgleichsgesetz verabschiedet, das zum Ausgleich der kalten Progression in der Einkommensteuer sowohl den Grundfreibetrag erhöht als auch die übrigen Tarifeckpunkte anpasst. Bei dem in Gesetzentwürfen verpflichtenden Hinweis auf Alternativen steht "keine". Doch es gibt sehr wohl eine Alternative zur Tarifverschiebung, schreiben Michael Broer und Sven Stöwhase. Sie besteht darin, allein den Grundfreibetrag zu erhöhen, was gemäß höchstrichterlicher Rechtsprechung zwingend notwendig ist. Die fiskalischen Einnahmen, die durch den Verzicht auf eine Anpassung der übrigen Tarifeckpunkte zustande kommen, werden dann über eine steuerpflichtige Kopfpauschale an die Steuerpflichtigen ausgeschüttet, ähnlich wie im Fall der Energiepauschale im Jahr 2022. Es zeigt sich beim Vergleich, dass gerade einkommensschwache Steuerzahler, die von der Inflation aktuell besonders belastet werden, von einer Kopfpauschalenlösung profitieren würden.
