Designing for the Long Tail of Machine Learning

• URL: http://arxiv.org/abs/2001.07455v1
• Date: Tue, 21 Jan 2020 11:53:28 GMT
• Title: Designing for the Long Tail of Machine Learning
• Authors: Martin Lindvall and Jesper Molin
• Abstract summary: We describe how machine learning performance scales with training data to guide designers in trade-offs between data gathering, model development and designing valuable interactions for a given model performance. We argue that a useful pattern is to design an initial system in a bootstrap phase that aims to exploit the training effect of data collected at increasing orders of magnitude.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent technical advances has made machine learning (ML) a promising component to include in end user facing systems. However, user experience (UX) practitioners face challenges in relating ML to existing user-centered design processes and how to navigate the possibilities and constraints of this design space. Drawing on our own experience, we characterize designing within this space as navigating trade-offs between data gathering, model development and designing valuable interactions for a given model performance. We suggest that the theoretical description of how machine learning performance scales with training data can guide designers in these trade-offs as well as having implications for prototyping. We exemplify the learning curve's usage by arguing that a useful pattern is to design an initial system in a bootstrap phase that aims to exploit the training effect of data collected at increasing orders of magnitude.

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