Marcelle: Composing Interactive Machine Learning Workflows and Interfaces

Abstract

Human-centered approaches to machine learning have established theoretical foundations, design principles and interaction techniques to facilitate end-user interaction with machine learning systems. Yet, general-purpose toolkits supporting the design of interactive machine learning systems are still missing, despite their potential to foster reuse, appropriation and collaboration between different stakeholders including developers, machine learning experts, designers and end users. In this paper, we present an architectural model for toolkits dedicated to the design of human interactions with machine learning. The architecture is built upon a modular collection of interactive components that can be composed to build interactive machine learning workflows, using reactive pipelines and composable user interfaces. We introduce Marcelle, a toolkit for the design of human interactions with machine learning that implements this model. We illustrate Marcelle with two implemented case studies: (1) a HCI researcher conducts user studies to understand novice interaction with machine learning, and (2) a machine learning expert and a clinician collaborate to develop a skin cancer diagnosis system. Finally, we discuss our experience with the toolkit, along with its limitation and perspectives.