Designing innovative research pathways for the advancement of design research: IASDR 2023 Doctoral and Postgraduate Consortium

The paper explores relevant themes for design research that arose from research works proposed for IASDR2023 and developed by doctoral candidates and recent master's degree graduates. Particular attention has been paid to research investigations that reflect on the theme of Life-Changing Design, specifically examining how design is responding to the transformations occurring in the contemporary period. Reflections on the soft impact of technologies, in particular digital technologies, on daily life are accompanied by an analysis of innovations and challenges faced by healthcare systems, products, and services. This is followed by an examination of social innovation themes and practices, and the development of new principles of inclusity. A concluding contribution highlights the requirement to identify innovative approaches to design education extending beyond recognized methodologies to implement personal and technical skills of new generations of designers

From ”Explainable AI” to ”Graspable AI”

Since the advent of Artificial Intelligence (AI) and Machine Learning (ML), researchers have asked how intelligent computing systems could interact with and relate to their users and their surroundings, leading to debates around issues of biased AI systems, ML black-box, user trust, user’s perception of control over the system, and system’s transparency, to name a few. All of these issues are related to how humans interact with AI or ML systems, through an interface which uses different interaction modalities. Prior studies address these issues from a variety of perspectives, spanning from understanding and framing the problems through ethics and Science and Technology Studies (STS) perspectives to finding effective technical solutions to the problems. But what is shared among almost all those efforts is an assumption that if systems can explain the how and why of their predictions, people will have a better perception of control and therefore will trust such systems more, and even can correct their shortcomings. This research field has been called Explainable AI (XAI). In this studio, we take stock on prior efforts in this area; however, we focus on using Tangible and Embodied Interaction (TEI) as an interaction modality for understanding ML. We note that the affordances of physical forms and their behaviors potentially can not only contribute to the explainability of ML systems, but also can contribute to an open environment for criticism. This studio seeks to both critique explainable ML terminology and to map the opportunities that TEI can offer to the HCI for designing more sustainable, graspable and just intelligent systems.QC 20210526</p