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Auflistung nach Autor:in "Anlauff, Charlotte"

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  • Zeitschriftenartikel
    Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers
    (i-com: Vol. 20, No. 1, 2021) Buschek, Daniel; Anlauff, Charlotte; Lachner, Florian
    This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.
  • Konferenzbeitrag
    Paper2Wire: a case study of user-centred development of machine learning tools for UX designers
    (Mensch und Computer 2020 - Tagungsband, 2020) Buschek, Daniel; Anlauff, Charlotte; Lachner, Florian
    This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.
  • Konferenzbeitrag
    Understanding Algorithms through Exploration: Supporting Knowledge Acquisition in Primary Tasks
    (Mensch und Computer 2019 - Tagungsband, 2019) Eiband, Malin; Anlauff, Charlotte; Ordenewitz, Tim; Zürn, Martin; Hussmann, Heinrich
    We investigate exploration as an alternative to explanation to improve user understanding of algorithms and algorithmic decision-making. Drawing on complex problem-solving as defined in cognitive science, we conducted a think-aloud study in the lab (N=10) as well as an MTurk online study (N=123) using a flight booking scenario to see if and how exploration supports \textit{knowledge acquisition} in two different tasks. One group was told to focus on booking the cheapest flight (knowledge acquisition as a secondary task), the other on understanding the system logic (knowledge acquisition as a primary task). Our results indicate that exploration, even as a secondary task, may contribute to knowledge about the underlying algorithm. However, our study also suggests that the overall knowledge acquired through exploration is limited in the sense that it gives people an idea of how a system works, rather than teaching them concrete rules they can recall. Overall, we conclude that exploration presents a design opportunity to interweave knowledge acquisition with users' primary task, and may thus contribute to (but not substitute) existing design solutions for supporting users in understanding algorithmic decision-making.