Deconstructivist Interaction Design: Interrogating Expression and Form

In this paper, we propose deconstructivist interaction design in order to facilitate the differentiation of an expressional vo- cabulary in interaction design. Based on examples that illus- trate how interaction design critically explores (i.e., decon- structs) its own expressional repertoire, we argue that there are commonalities with deconstructivist phases in related de- sign disciplines to learn from. Therefore, we draw on the role and characteristics of deconstructivism in the history of archi- tecture, graphic design, and fashion. Afterwards, we reflect on how interaction design is already a means of deconstruc- tion (e.g., in critical design). Finally, we discuss the potential of deconstructivism for form-giving practices, resulting in a proposal to extend interaction design’s expressional vocabu- lary of giving form to computational material by substantiat- ing a deconstructivist perspective.

Using Design Fiction To Reflect on Autonomy in Smart Technology For People Living With Dementia

The field of HCI is changing, which brings with it new responsibilities. Ubiquitous computing touches on many aspects of modern life and its consequences are not yet fully understood. In the context of dementia ubiquitous technologies are currently developed to augment care and thereby enhancing quality of life for people living with dementia as well as reducing the financial pressures on the health care system. Within this paper a design fiction is presented as a method to explore the issues that may arise from the new technologies in this context. It introduces the idea of replacing Smart Home technology with wearable solutions to observe the technologies more critically through defamiliarization and use these observations to feed back into technology design

Designing AI Experiences: Boundary Representations, Collaborative Processes, and Data Tools

Artificial Intelligence (AI) has transformed our everyday interactions with technology through automation, intelligence augmentation, and human-machine partnership. Nevertheless, we regularly encounter undesirable and often frustrating experiences due to AI. A fundamental challenge is that existing software practices for coordinating system and experience designs fall short when creating AI for diverse human needs, i.e., ``human-centered AI'' or HAI. ``AI-first'' development workflows allow engineers to first develop the AI components, and then user experience (UX) designers create end-user experiences around the AI's capabilities. Consequently, engineers encounter end-user blindness when making critical decisions about AI training data needs, implementation logic, behavior, and evaluation. In the conventional ``UX-first'' process, UX designers lack the needed technical understanding of AI capabilities (technological blindness) that limits their ability to shape system design from the ground up. Human-AI design guidelines have been offered to help but neither describe nor prescribe ways to bridge the gaps in needed expertise in creating HAI. In this dissertation, I investigate collaboration approaches between designers and engineers to operationalize the vision for HAI as technology inspired by human intelligence that augments human abilities while addressing societal needs. In a series of studies combining technical HCI research with qualitative studies of AI production in practice, I contribute (1) an approach to software development that blurs rigid design-engineering boundaries, (2) a process model for co-designing AI experiences, and (3) new methods and tools to empower designers by making AI accessible to UX designers. Key findings from interviews with industry practitioners include the need for ``leaky'' abstractions shared between UX and AI designers. Because modular development and separation of concerns fail with HAI design, leaky abstractions afford collaboration across expertise boundaries and support human-centered design solutions through vertical prototyping and constant evaluation. Further, by observing how designers and engineers collaborate on HAI design in an in-lab study, I highlight the role of design `probes' with user data to establish common ground between AI system and UX design specifications, providing a critical tool for shaping HAI design. Finally, I offer two design methods and tool implementations --- Data-Assisted Affinity Diagramming and Model Informed Prototyping --- for incorporating end-user data into HAI design. HAI is necessarily a multidisciplinary endeavor, and human data (in multiple forms) is the backbone of AI systems. My dissertation contributions inform how stakeholders with differing expertise can collaboratively design AI experiences by reducing friction across expertise boundaries and maintaining agency within team roles. The data-driven methods and tools I created provide direct support for software teams to tackle the novel challenges of designing with data. Finally, this dissertation offers guidance for imagining future design tools for human-centered systems that are accessible to diverse stakeholders.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169917/1/harihars_1.pd