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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