A case study investigating a user-centred and expert informed `companion guide' for a complex sensor-based platform

Rachel Eardley*, Sue Mackinnon, Emma L. Tonkin, Ewan Soubutts, Amid Ayobi, Jess Linington, Gregory J. L. Tourte, Zoe Banks Gross, David Bailey, Russell G Knights, Rachael Gooberman-Hill, Ian J Craddock, Aisling Ann O'Kane

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)

Abstract

We present a case study that informs the creation of a `companion guide' providing transparency to potential non-expert users of a ubiquitous machine learning (ML) platform during the initial onboarding. Ubiquitous platforms (e.g., smart home systems, including smart meters and conversational agents) are increasingly commonplace and increasingly apply complex ML methods. Understanding how non-ML experts comprehend these platforms is important in supporting participants in making an informed choice about if and how they adopt these platforms. To aid this decision-making process, we created a companion guide for a home health platform through an iterative user-centred-design process, seeking additional input from platform experts at all stages of the process to ensure the accuracy of explanations. This user-centred and expert informed design process highlights the need to present the platform's entire ecosystem at an appropriate level for those with differing backgrounds to understand, in order to support informed consent and decision making.
Original languageEnglish
Article number93
Number of pages23
JournalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)
Volume6
Issue number2
DOIs
Publication statusPublished - 7 Jul 2022

Bibliographical note

Funding Information:
(开is work was performed under the SPHERE IRC funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EPK031910/1.

Publisher Copyright:
© 2022 ACM.

Structured keywords

  • SPHERE
  • Digital Health

Keywords

  • Machine learning
  • Onboarding
  • Smart home
  • Health
  • Case study
  • Design Process

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