Hidden Figures: Architectural Challenges to Expose Parameters Lost in Code

Alan Dix*

*Corresponding author for this work

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

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Abstract

Many critical user interaction design decisions are made in the heat of detailed development. These include simple parameter choices or more complex weightings in intelligent algorithms. Many would be appropriate for expert design review, user-preference choices or optimisation by machine learning, but they are buried deep in the code. Although the developer may realise this potential, the location of the decision is far removed in the code from where user feedback occurs, data can be collected and machine learning could be applied. This position paper describes several case studies and uses them to frame an architectural challenge for tools and infrastructure to uncover these hidden variables to make them available for machine learning and user inspection.

Original languageEnglish
Title of host publicationEngineering Interactive Computer Systems. EICS 2023 International Workshops and Doctoral Consortium - Selected Papers
EditorsMichael Harrison, Célia Martinie, Nicholas Micallef, Philippe Palanque, Albrecht Schmidt, Marco Winckler, Enes Yigitbas, Luciana Zaina
PublisherSpringer Science and Business Media Deutschland GmbH
Pages116-126
Number of pages11
ISBN (Print)9783031592348
DOIs
Publication statusPublished - 8 Aug 2024
Event15th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, EICS 2023 - Swansea, United Kingdom
Duration: 26 Jun 202327 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14517 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, EICS 2023
Country/TerritoryUnited Kingdom
CitySwansea
Period26/06/2327/06/23

Keywords

  • Intelligent interfaces
  • machine learning
  • user interface architecture

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