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inML Kit: empowering the prototyping of ML-enhanced products by involving designers in the ML lifecycle

Published online by Cambridge University Press:  09 February 2022

Lingyun Sun
Affiliation:
State Key Laboratory of CAD&CG at Zhejiang University, Hangzhou, China Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Zhejiang University, Hangzhou, China
Yuyang Zhang
Affiliation:
Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Zhejiang University, Hangzhou, China
Zhuoshu Li
Affiliation:
State Key Laboratory of CAD&CG at Zhejiang University, Hangzhou, China Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Zhejiang University, Hangzhou, China
Zihong Zhou
Affiliation:
Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Zhejiang University, Hangzhou, China
Zhibin Zhou*
Affiliation:
Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Zhejiang University, Hangzhou, China
*
Author for correspondence: Zhibin Zhou, E-mail: zhibinzhou@zju.edu.cn

Abstract

Machine learning (ML) is increasingly used to enhance intelligent products in the field of product design. However, ML has a never-ending lifecycle in which its capabilities and technical properties iteratively change as new annotated data are utilized. The never-ending lifecycle of ML (which includes data annotation, model training, and other steps) has led to challenges to the prototyping of ML-enhanced products and requires a high level of ML literacy in designers. To facilitate the prototyping of ML-enhanced products and improve the ML literacy of designers, we draw inspiration from a design method called Material Lifecycle Thinking (MLT), which regards ML as a continuously evolving design material. Based on the MLT, we proposed a cyclical prototype workflow and developed inML Kit, a toolkit enabling designers to make functional ML prototypes and improve ML literacy by involving them in the never-ending ML lifecycle. The toolkit was designed, iterated, and implemented through the participatory design process with experienced designers in this field. We evaluated inML Kit by conducting a controlled user study where our toolkit was compared with Google AIY. The evaluation results imply that our inML Kit helps designers to make functional ML prototypes while improving their ML literacy.

Type
Research Article
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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