Machine Learning (ML) is increasingly becoming a crucial asset across diverse industries. However, designers lack human-centered processes to envision and develop innovative solutions enabled by ML. By engaging in a Research-through-Design activity, we outline a new design process to generate human-centered adaptive systems enabled by data and ML. We describe and discuss the possibilities and limits of designing with ML, the need to concurrently address user experience and ML aspects, and the implications of their mutual influence. We argue that designers can envision and design human-centered ML-enabled systems if they acquire fundamental ML knowledge, although certain tasks necessitate close collaboration with ML experts. We discuss how uncertainty and risk of failure characterize the outlined process and may limit its applicability. The proposed process serves as a foundational framework for future research in human-centered design innovation through data and ML.@en

Designing with data and Artificial Intelligence (AI) can bring significant value to the development of systems and technologies that promote personal wellbeing. However, there are also unaddressed challenges and risks connected to designing (with) AI for wellbeing, such as the difficulties in ensuring that the generated feedback or proposed interventions are relevant considering the large interpersonal variations between the current, desired and achievable level of physical and mental wellbeing of different individuals. In this one-day hybrid workshop, we aim to bring together design and HCI researchers and practitioners interested in the intersection of design, AI, and wellbeing beyond clinical applications. We will discuss challenges in designing with AI for wellbeing originating from a) the domains of design and b) general issues in developing AI systems, and uncover new potential directions that emerge when coupling design, AI and wellbeing. Our aim is to bring together researchers and practitioners from various fields and backgrounds who use data and AI when designing for wellbeing. Through this workshop, we aim to create a conceptual framework that enables the emergence of rich, meaningful, and ethical solutions for designing (with) AI for wellbeing, while also providing handles to mitigate the emergence of negative consequences.@en

Ambiguity is gaining attention in self-tracking research as a means to go beyond the mere quantification of body signals. Recent research has suggested that ambiguity can be used even to enable social connection mediated by personal data. To explore this design space more widely, we organized two design workshops with a total of 67 participants. In this paper, we present three design concepts, as outcomes of the workshops, which use ambiguity to enable social self-tracking practices. We then discuss how these concepts demonstrate the potential of ambiguity to encourage collective sense-making, directly impact the user's social relationships, and offer multifaceted perspectives on reality.

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In the COVID-19 pandemic, digital technologies (DT) supported the design and implementation of solutions addressing new needs and living conditions. We describe Design for Emergency, a digital open design platform developed to ideate solutions for people's fast-changing needs in the pandemic, to analyze how DT can affect human-centered design processes during emergencies. We illustrate how DT: i) helped quickly collect and analyse people's needs in different countries, visualize such data, and identify design directions and problem spaces; ii) facilitated the creation of a virtual network of stakeholders and an open-innovation digital platform; iii) inspired the ideation of solutions responding to people's changing needs and affected their implementation. We discuss the implications of adopting DT in designing for and during emergencies, as well as their current and future potential to promptly respond to emergency situations through a human-centered approach.@en