%0 Generic %1 lindvall2020designing %A Lindvall, Martin %A Molin, Jesper %D 2020 %K 2020 arxiv machine-learning statistics %T Designing for the Long Tail of Machine Learning %U http://arxiv.org/abs/2001.07455 %X 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.

@misc{lindvall2020designing, 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.}, added-at = {2020-01-28T12:07:45.000+0100}, author = {Lindvall, Martin and Molin, Jesper}, biburl = {https://www.bibsonomy.org/bibtex/2761f64d5583555c402323f3dbc98934b/analyst}, description = {[2001.07455] Designing for the Long Tail of Machine Learning}, interhash = {cbe76428f8ec5da4c4782185248c307b}, intrahash = {761f64d5583555c402323f3dbc98934b}, keywords = {2020 arxiv machine-learning statistics}, note = {cite arxiv:2001.07455Comment: Accepted for presentation in poster format for the ACM CHI'19 Workshop <Emerging Perspectives in Human-Centered Machine Learning>}, timestamp = {2020-01-28T12:07:45.000+0100}, title = {Designing for the Long Tail of Machine Learning}, url = {http://arxiv.org/abs/2001.07455}, year = 2020 }