Visualizing Ubiquitously Sensed Measures of Motor Ability in Multiple Sclerosis

Abstract

Sophisticated ubiquitous sensing systems are being used to measure motor ability in clinical settings. Intended to augment clinical decision-making, the interpretability of the machine-learning measurements underneath becomes critical to their use. We explore how visualization can support the interpretability of machine-learning measures through the case of Assess MS, a system to support the clinical assessment of Multiple Sclerosis. A substantial design challenge is to make visible the algorithm's decision-making process in a way that allows clinicians to integrate the algorithm's result into their own decision process. To this end, we present a series of design iterations that probe the challenges in supporting interpretability in a real-world system. The key contribution of this article is to illustrate that simply making visible the algorithmic decision-making process is not helpful in supporting clinicians in their own decision-making process. It disregards that people and algorithms make decisions in different ways. Instead, we propose that visualisation can provide context to algorithmic decision-making, rendering observable a range of internal workings of the algorithm from data quality issues to the web of relationships generated in the machine-learning process.