@inbook{10.1145/3419249.3420163,
author = {Chromik, Michael and Lachner, Florian and Butz, Andreas},
title = {ML for UX? - An Inventory and Predictions on the Use of Machine Learning Techniques for UX Research},
year = {2020},
isbn = {9781450375795},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3419249.3420163},
abstract = {Machine learning (ML) techniques have successfully been applied to many complex domains. Yet, applying it to UX research (UXR) received little academic attention so far. To better understand how UX practitioners envision the synergies between empathy-focused UX work and data-driven ML techniques, we surveyed 49 practitioners experienced in UX, ML, or both and conducted 13 semi-structured interviews with UX experts. We derived an inventory of ML’s impact on current UXR activities and practitioners’ predictions about its potentials. We learned that ML methods may help to automate mundane tasks, complement decisions with data-driven insights, and enrich UXR with insights from users’ emotional worlds. Challenges may arise from a potential obligation to utilize data and a more restrictive access to user data. We embed our insights into recent academic work on ML for UXR and discuss automated UX evaluation as a promising use case for future research. },
booktitle = {Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society},
articleno = {57},
numpages = {11}
}