Tytuł pozycji:
Successfully Improving the User Experience of an Artificial Intelligence System
An important aspect of Artificial Intelligence (AI) Systems is their User Experience (UX), which can impact the user's trust in the AI system. However, UX has not yet been in the focus of AI research. In previous research, we have evaluated the UX of the Meta AutoML platform OMA-ML, uncovering weak points and proposing several recommendations for ensuring a positive UX in AI systems. In this paper we show that implementing those recommendations leads to measurable UX improvements. We present the UX-improving features implemented in a new release of OMA-ML and the results from a second UX evaluation. The UX of OMA-ML could successfully be improved in four interactive principles (suitability for the user's tasks, self-descriptiveness, user engagement and learnability). We argue that an iterative approach to UX potentially leads to more human-centered AI.
1. This work is funded by the German federal ministry of education and research (BMBF) in the program Zukunft der Wertschöpfung (funding code 02L19C157), and supported by Projektträger Karlsruhe (PTKA).
2. Main Track: Short Papers
3. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
