Towards Guidelines for Designing Human-in-the-Loop Machine Training Interfaces

A.R. van der Stappen, Mathias Funk

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Supervised machine learning approaches commonly require good availability and quality of training data. In applications that depend on human-labeled data, especially from experts, or that depend on contextual knowledge for training data sets, the human-in-the- loop presents a serious bottleneck to the scalability of training efforts. Even if human labeling is generally feasible, sustained hu- man performance and high-quality labels in larger quantities are challenging. Interactive Machine Learning can help solve usability problems in traditional machine learning by giving users agency in deciding how systems learn from data. Yet, the field lacks clear design guidelines for such interfaces, specifically regarding the scaling of training processes. In this paper, we present results from a pilot study in which participants interacted with several inter- face variants of a recommender engine and evaluated them on interaction and efficiency parameters. Based on the performance of these different learning system implementations we propose design guidelines for the design of such systems and a score for compara- tive evaluation, in which we combine interaction experience and system learning efficiency into one relative scoring unit.
Original languageEnglish
Title of host publication26th International Conference on Intelligent User Interfaces
PublisherAssociation for Computing Machinery, Inc
Pages514-519
Number of pages6
Publication statusPublished - 2021
EventIUI '21: 26th International Conference on Intelligent User Interfaces - College Station, TX, United States
Duration: 13 Apr 202117 Apr 2021
Conference number: 26
https://iui.acm.org/2021/

Conference

ConferenceIUI '21: 26th International Conference on Intelligent User Interfaces
Abbreviated titleIUI'21
CountryUnited States
CityCollege Station, TX
Period13/04/2117/04/21
Internet address

Keywords

  • Machine Learning
  • Labeling Data
  • Algorithmic Training
  • Interaction
  • User Interface
  • UX Metric

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