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 language | English |
|---|---|
| Article number | 93 |
| Number of pages | 23 |
| Journal | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) |
| Volume | 6 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 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