Mix and Match Machine Learning: An Ideation Toolkit to Design Machine Learning-Enabled Solutions

Abstract

Machine learning (ML) provides designers with a wide range of opportunities to innovate products and services. However, the design discipline struggles to integrate ML knowledge in education and prepare designers to ideate with ML. We propose the Mix and Match Machine Learning toolkit, which provides relevant ML knowledge in the form of tangible tokens and a web interface to support designers' ideation processes. The tokens represent data types and ML capabilities. By using the toolkit, designers can explore, understand, combine, and operationalize the capabilities of ML and understand its limitations, without depending on programming or computer science knowledge. We evaluated the toolkit in two workshops with design students, and we found that it supports both learning and ideation goals. We discuss the design implications and potential impact of a hybrid toolkit for ML on design education and practice.