@inproceedings{yang2018grounding,
author = {Yang, Qian and Suh, Jina and Chen, Nan-Chen and Ramos, Gonzalo},
title = {Grounding Interactive Machine Learning Tool Design in How Non-Experts Actually Build Models},
booktitle = {Proceeding of the 2018 Designing of Interactive Systems Conference},
year = {2018},
month = {June},
abstract = {Machine learning (ML) promises data-driven insights and solutions for people from all walks of life, but the skill of crafting these solutions is possessed by only a few. Emerging research addresses this issue by creating ML tools that are easy and accessible to people who are not formally trained in ML (“non-experts”). This work investigated how non-experts build ML solutions for themselves in real life. Our interviews and surveys revealed unique potentials of non-expert ML, as well several pitfalls that non-experts are susceptible to. For example, many perceived percentage accuracy as a sole measure of performance, thus problematic models proceeded to deployment. These observations suggested that, while challenging, making ML easy and robust should both be important goals of designing novice-facing ML tools. To advance on this insight, we discuss design implications and created a sensitizing concept to demonstrate how designers might guide non-experts to easily build robust solutions.},
publisher = {ACM},
url = {http://approjects.co.za/?big=en-us/research/publication/grounding-interactive-machine-learning-tool-design-non-experts-actually-build-models/},
}