M. Lindvall, und J. Molin. (2020)cite arxiv:2001.07455Comment: Accepted for presentation in poster format for the ACM CHI'19 Workshop <Emerging Perspectives in Human-Centered Machine Learning>.
Zusammenfassung
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.
Beschreibung
[2001.07455] Designing for the Long Tail of Machine Learning
cite arxiv:2001.07455Comment: Accepted for presentation in poster format for the ACM CHI'19 Workshop <Emerging Perspectives in Human-Centered Machine Learning>
%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
}