Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Data sovereignty requirements for patient-oriented AI-driven clinical research in Germany

2024 , Radic, Marija , Busch-Casler, Julia , Vosen, Agnes , Herrmann, Philipp , Appenzeller, Arno , Mucha, Henrik , Philipp, Patrick , Frank, Kevin , Dauth, Stephanie , Köhm, Michaela , Orak, Berna , Spiecker genannt Döhmann, Indra , Böhm, Peter

The rapidly growing quantity of health data presents researchers with ample opportunity for innovation. At the same time, exploitation of the value of Big Data poses various ethical challenges that must be addressed in order to fulfil the requirements of responsible research and innovation (Gerke et al. 2020; Howe III and Elenberg 2020). Data sovereignty and its principles of self-determination and informed consent are central goals in this endeavor. However, their consistent implementation has enormous consequences for the collection and processing of data in practice, especially given the complexity and growth of data in healthcare, which implies that artificial intelligence (AI) will increasingly be applied in the field due to its potential to unlock relevant, but previously hidden, information from the growing number of data (Jiang et al. 2017). Consequently, there is a need for ethically sound guidelines to help determine how data sovereignty and informed consent can be implemented in clinical research. Using the method of a narrative literature review combined with a design thinking approach, this paper aims to contribute to the literature by answering the following research question: What are the practical requirements for the thorough implementation of data sovereignty and informed consent in healthcare? We show that privacy-preserving technologies, human-centered usability and interaction design, explainable and trustworthy AI, user acceptance and trust, patient involvement, and effective legislation are key requirements for data sovereignty and self-determination in clinical research. We outline the implications for the development of IT solutions in the German healthcare system.

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PersonalAIzation - Exploring concepts and guidelines for AI-driven personalization of in-car HMIs in fully automated vehicles

2022 , Sundar, Shrivaas Madapusi , Bopp-Bertenbreiter, Valeria , Ziegler, Daniel , Kosuru, Ravi Kanth , Knecht, Christian , Pfleging, Bastian , Widlroither, Harald , Diederichs, Frederik

The role of the driver changes to that of a passenger in autonomous cars. Thus, the vehicle interior transforms from a cockpit into a multimedia station and workspace. This work explores concepts for Artificial Intelligence (AI) to provide a personalized user experience for the passengers in the form of Contextual Personalized Shortcuts and Personalized Services in the infotainment system. The two use cases were iteratively developed based on literature research and surveys. We evaluated AI- Personalized Services and compared AI-generated to the manually configurable shortcuts. AttrakDiff (Hassenzahl et al., 2003) and Car Technology Acceptance Model (CTAM; Osswald et al., 2012) were used to evaluate UX and user acceptance. The AI-Personalized interface obtained positive scores and reactions in the user testing and shows potential. Based on the insight from the user studies and literature review, we present and human-AI interaction guidelines to build effective AI-personalized HMIs.

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Towards AI-Enabled Assistant Design Through Grassroots Modeling: Insights from a Practical Use Case in the Industrial Sector

2022 , Dhiman, Hitesh , Fellmann, Michael , Röcker, Carsten

Process modeling is used to understand a business process and to document requirements, but is mostly formalized and limited to modeling experts. This can be a problem when designing interactive systems that incorporate AI elements, since the design can fail to take into account tacit knowledge and context-specific requirements of people that execute the process. While recent discourse has highlighted this gap and called for an exploration into light-weight, grassroots modeling techniques that can be used to model everyday work, it is still unclear how these can be harnessed to design information systems that support work. The aim of this paper is to showcase how a triangulated approach combining three different perspectives - grassroots modeling, theoretical grounding, and first person media, can be used to collaboratively model an informal work activity and design an AI-enabled system to instruct novices to perform that activity. Our experience confirms the assertion that, when provided with the necessary scaffolding, experts without any formal modeling experience can be supported to model their specific, local activities and, in doing so, contribute valuable knowledge to the design of information systems.

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Artificial Intelligence for Adaptive, Responsive, and Level-Compliant Interaction in the Vehicle of the Future (KARLI)

2022 , Diederichs, Frederik , Wannemacher, Christoph , Faller, Fabian , Schmidt, Eike , Engelhardt, Doreen , Mikolajewski, Martin , Rittger, Lena , Voit, Michael , Widlroither, Harald , Martin, Manuel , Hashemi, Vahid , Sahakyan, Manya , Romanelli, Massimo , Kiefer, Bernd , Fäßler, Victor , Rößler, Tobias , Großerüschkamp, Marc , Kurbos, Andreas , Bottesch, Miriam , Immoor, Pia , Engeln, Arnd , Fleischmann, Marlis , Schweiker, Miriam , Pagenkopf, Anne , Daniela Piechnik , Mathis, Lesley-Ann

The KARLI project consortium investigates and develops monitoring systems for drivers and other occupants with new artificial intelligence approaches, based on high quality labeled data that is collected in real vehicles. The project’s target applications are integrated in vehicles that enable various levels of automation and transitions of control. Level-compliant occupant behavior is assessed with AI algorithms and modulated with responsive and adaptive human machine interface (HMI) solutions. The project also targets the prediction and prevention of motion sickness in order to improve the user experience, enabling productivity and maintaining an adequate driver state. The user-centered approach is represented by defining five KARLI User Roles which specify the driving related behavior requirements for all levels of automation. The project results will be evaluated at the end of the project. The KARLI applications will be evaluated regarding user experience benefits and AI performance measures. The KARLI project is approaching two main challenges that are ambitious and have a high potential: First, raising and investigating the potential of AI for driver monitoring and driver-vehicle interaction, and second, accelerating the transfer from research to series production applications.

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