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.

Gaining an understanding of people's diverse mental health needs is essential for informing the design of inclusive mental health technologies. However, conversations about mental health experiences can be challenging for both researchers and participants. We present the design of visual cards that illustrate an inclusive mental health concept to support researchers and participants in understanding and sharing mental health experiences. We illustrate the iterative design of the visual cards with our reflections and feedback from ethnically diverse participants. We found that designing the visual cards fostered insightful reflections within the design team regarding the roles of identity, gender, and ethnicity in designing culturally sensitive content and research. Participants from minority ethnic backgrounds valued the illustrative elements of the visual cards and highlighted the importance of supporting different languages and visual cultures. We discuss use cases for the visual cards and implications for designing culturally sensitive mental health technologies.