Making machine learning useable
Jianlong J. Zhou, Fang Chen · International Journal of Intelligent Systems Technologies and Applications · 2015
Despite the recognised value of machine learning (ML) techniques and high expectation of applying ML techniques within various applications, users often find it difficult to effectively apply ML techniques in practise because of complicated interfaces between ML algorithms and users. This paper focuses on investigating making ML useable from the point of view of how human-computer interaction (HCI) techniques benefit ML in order to simplify the interface between users and ML algorithms. We formulate possible research directions in making ML useable based on human factors, decision making and trust in ML. We strongly believe that a trustworthy decision making based on ML results, which is the ultimate goal of ML-based applications, contributes to the overall application performance and makes ML more useable. Two case studies of measurable decision making and revealing internal states of ML process are presented to show how HCI techniques are used to make ML useable.