Showing papers by "Colleen M. Seifert published in 2021"
28 Jun 2021
TL;DR: In this article, the authors investigate the co-creation process through a design study with 10 pairs of designers and engineers and find that design "probes" with user data are a useful tool in defining AI materials.
Abstract: Thinking of technology as a design material is appealing. It encourages designers to explore the material’s properties to understand its capabilities and limitations—a prerequisite to generative design thinking. However, as a material, AI resists this approach because its properties only emerge as part of the user experience design. Therefore, designers and AI engineers must collaborate in new ways to create both the material and its application experience. We investigate the co-creation process through a design study with 10 pairs of designers and engineers. We find that design ‘probes’ with user data are a useful tool in defining AI materials. Through data probes, designers construct designerly representations of the envisioned AI experience (AIX) to identify desirable AI characteristics. Data probes facilitate divergent design thinking, material testing, and design validation. Based on our findings, we propose a process model for co-creating AIX and offer design considerations for incorporating data probes in AIX design tools.
24 citations
14 Apr 2021
TL;DR: ProtoAI as discussed by the authors is a workflow for AIX design that combines model exploration with UI prototyping tasks, allowing designers to directly incorporate model outputs into interface designs, evaluate design choices across different inputs, and iteratively revise designs by analyzing model breakdowns.
Abstract: When prototyping AI experiences (AIX), interface designers seek useful and usable ways to support end-user tasks through AI capabilities. However, AI poses challenges to design due to its dynamic behavior in response to training data, end-user data, and feedback. Designers must consider AI’s uncertainties and offer adaptations such as explainability, error recovery, and automation vs. human task control. Unfortunately, current prototyping tools assume a black-box view of AI, forcing designers to work with separate tools to explore machine learning models, understand model performance, and align interface choices with model behavior. This introduces friction to rapid and iterative prototyping. We propose Model-Informed Prototyping (MIP), a workflow for AIX design that combines model exploration with UI prototyping tasks. Our system, ProtoAI, allows designers to directly incorporate model outputs into interface designs, evaluate design choices across different inputs, and iteratively revise designs by analyzing model breakdowns. We demonstrate how ProtoAI can readily operationalize human-AI design guidelines. Our user study finds that designers can effectively engage in MIP to create and evaluate AI-powered interfaces during AIX design.
16 citations
TL;DR: For example, the authors conducted an empirical study of member submissions to a successful conservation campaign, the Monarch Story Campaign, conducted by the Environmental Defense Fund (EDF), and found that people often described encounters with monarchs in childhood and as adults.
Abstract: What makes a flagship species effective in engaging conservation donors? Large, charismatic mammals are typically selected as ambassadors, but a few studies suggest butterflies—and monarchs in particular—may be even more appealing. To gather more information about people’s responses to monarchs, we conducted an empirical study of member submissions to a successful conservation campaign, the Monarch Story Campaign, conducted by the Environmental Defense Fund (EDF). The set of 691 stories along with their associated demographic and donation data was analyzed in a mixed-methods study using qualitative analysis and tests of association. The results showed that people often described encounters with monarchs in childhood and as adults. They expressed strong, positive emotions, and lauded the monarch’s beauty and other “awe-inspiring” qualities and expressed wonder at their lifecycle (i.e., metamorphosis and migration). They also raised conservation themes of distress at monarch loss, calls for action, and caretaking, such as being “fragile” and “in need.” Sharing personal encounters was associated with current efforts to save the species and more past financial donations, while a second pattern tied more donations to awe at the monarch’s mass migration. These results imply that conservation campaigns built around species people encounter may build lifelong awareness, concern, and actions towards conservation.
8 citations
TL;DR: A tool to support solution mapping was created and its impact with engineering students was tested, finding more diverse problem applications were produced when using the tool.
Abstract: Engineering design processes are often defined as beginning with a problem and diverging to generate possible solutions; however, design processes can start with a newly developed technological sol...
6 citations
TL;DR: Daniel et al. as discussed by the authors used a case study design informed by an ED-specific diagnostic decision-making model (the modified ED-National Academies of Sciences, Engineering and Medicine (NASEM) model) and two cognitive theories (dual process theory and distributed cognition).
Abstract: Author(s): Daniel, Michelle; Park, SunYoung; Seifert, Colleen M; Chandanabhumma, P Paul; Fetters, Michael D; Wilson, Eric; Singh, Hardeep; Pasupathy, Kalyan; Mahajan, Prashant | Abstract: IntroductionDiagnostic processes in the emergency department (ED) involve multiple interactions among individuals who interface with information systems to access and record information. A better understanding of diagnostic processes is needed to mitigate errors. This paper describes a study protocol to map diagnostic processes in the ED as a foundation for developing future error mitigation strategies.Methods and analysisThis study of an adult and a paediatric academic ED uses a prospective mixed methods case study design informed by an ED-specific diagnostic decision-making model (the modified ED-National Academies of Sciences, Engineering and Medicine (NASEM) model) and two cognitive theories (dual process theory and distributed cognition). Data sources include audio recordings of patient and care team interactions, electronic health record data, observer field notes and stakeholder interviews. Multiple qualitative analysis methods will be used to explore diagnostic processes in situ, including systems information flow, human-human and human-system interactions and contextual factors influencing cognition. The study has three parts. Part 1 involves prospective field observations of patients with undifferentiated symptoms at high risk for diagnostic error, where each patient is followed throughout the entire care delivery process. Part 2 involves observing individual care team providers over a 4-hour window to capture their diagnostic workflow, team coordination and communication across multiple patients. Part 3 uses interviews with key stakeholders to understand different perspectives on the diagnostic process, as well as perceived strengths and vulnerabilities, in order to enrich the ED-NASEM diagnostic model.Ethics and disseminationThe University of Michigan Institutional Review Board approved this study, HUM00156261. This foundational work will help identify strengths and vulnerabilities in diagnostic processes. Further, it will inform the future development and testing of patient, provider and systems-level interventions for mitigating error and improving patient safety in these and other EDs. The work will be disseminated through journal publications and presentations at national and international meetings.
2 citations
Posted Content•
TL;DR: In this paper, the authors investigate the co-creation process through a design study with 10 pairs of designers and engineers, and propose a process model for co-creating AIX and offer design considerations for incorporating data probes in design tools.
Abstract: Thinking of technology as a design material is appealing. It encourages designers to explore the material's properties to understand its capabilities and limitations, a prerequisite to generative design thinking. However, as a material, AI resists this approach because its properties emerge as part of the design process itself. Therefore, designers and AI engineers must collaborate in new ways to create both the material and its application experience. We investigate the co-creation process through a design study with 10 pairs of designers and engineers. We find that design 'probes' with user data are a useful tool in defining AI materials. Through data probes, designers construct designerly representations of the envisioned AI experience (AIX) to identify desirable AI characteristics. Data probes facilitate divergent thinking, material testing, and design validation. Based on our findings, we propose a process model for co-creating AIX and offer design considerations for incorporating data probes in design tools.