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PublicationTowards AI-Enabled Assistant Design Through Grassroots Modeling: Insights from a Practical Use Case in the Industrial Sector( 2022)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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PublicationArtificial Intelligence for Adaptive, Responsive, and Level-Compliant Interaction in the Vehicle of the Future (KARLI)( 2022)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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PublicationPersonalAIzation - Exploring concepts and guidelines for AI-driven personalization of in-car HMIs in fully automated vehicles( 2022)
