InML Kit: Empowering the prototyping of ML-enhanced products by involving designers in the ML lifecycle

Lingyun Sun, Yuyang Zhang, Zhuoshu Li, Zihong Zhou, Zhibin Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Article numbere8
JournalArtificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Volume36
DOIs
Publication statusPublished - 9 Feb 2022
Externally publishedYes

Keywords

  • Artificial intelligence
  • Design
  • Lifecycle
  • Machine learning
  • Prototype tool

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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