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1. Creation and Characterization of Design Spaces

   Gero, John S ; Milovanovic, Julie ( July 2022 , DRS)

   Lockton, Dan ; Lenzi, Sara (Ed.)

   Designers advance in the design processes by creating and expanding the design space where the solution they develop unfolds. This process requires the co- evolution of the problem and the solution spaces through design state changes. In this paper, we provide a methodology to capture how designers create, structure and expand their design space across time. Design verbalizations from a team of three professional engineers are coded into design elements from the Function-Behavior- Structure ontology to identify the characteristics of design state changes. Three types of changes can occur: a change within the problem space, a change within the solution space or a change between the problem and the solution spaces or inversely. The paper explores how to represent such changes by generating a network of design concepts. By tracking the evolution of the design space over time, we represent how the design space expands as the design activity progresses. 

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2. Analyzing Value Discovery in Design Decisions Through Ethicography

   https://doi.org/10.1145/3290605.3300307  

   Chivukula, Shruthi Sai ; Gray, Colin M. ; Brier, Jason A. ( May 2019 , Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19)

   HCI scholarship is increasingly concerned with the ethical impact of socio-technical systems. Current theoretically driven approaches that engage with ethics generally prescribe only abstract approaches by which designers might consider values in the design process. However, there is little guidance on methods that promote value discovery, which might lead to more specific examples of relevant values in specific design contexts. In this paper, we elaborate a method for value discovery, identifying how values impact the designer's decision making. We demonstrate the use of this method, called Ethicography, in describing value discovery and use throughout the design process. We present analysis of design activity by user experience (UX) design students in two lab protocol conditions, describing specific human values that designers considered for each task, and visualizing the interplay of these values. We identify opportunities for further research, using the Ethicograph method to illustrate value discovery and translation into design solutions. 

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3. What Kind of Work Do "Asshole Designers" Create? Describing Properties of Ethical Concern on Reddit

   https://doi.org/10.1145/3357236.3395486  

   Gray, Colin M. ; Chivukula, Shruthi Sai ; Lee, Ahreum ( July 2020 , DIS '20: Proceedings of the 2020 ACM on Designing Interactive Systems Conference)

   Design practitioners are increasingly engaged in describing ethical complexity in their everyday work, exemplified by concepts such as "dark patterns" and "dark UX." In parallel, researchers have shown how interactions and discourses in online communities allow access to the various dimensions of design complexity in practice. In this paper, we conducted a content analysis of the subreddit "/r/assholedesign," identifying how users on Reddit engage in conversation about ethical concerns. We identify what types of artifacts are shared, and the salient ethical concerns that community members link with "asshole" behaviors. Based on our analysis, we propose properties that describe "asshole designers," both distinct and in relation to dark patterns, and point towards an anthropomorphization of ethics that foregrounds the inscription of designer's values into designed outcomes. We conclude with opportunities for further engagement with ethical complexity in online and offline contexts, stimulating ethics-focused conversations among social media users and design practitioners. 

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4. Generative Design: Reframing the Role of the Designer in Early-Stage Design Process

   https://doi.org/10.1115/1.4056799  

   Saadi, Jana I. ; Yang, Maria C. ( April 2023 , Journal of Mechanical Design)

   Generative design tools empowered by recent advancements in artificial intelligence (AI) offer the opportunity for human designers and design tools to collaborate in new, more advanced modes throughout various stages of the product design process to facilitate the creation of higher performing and more complex products. This paper explores how the use of these generative design tools may impact the design process, designer behavior, and overall outcomes. Six in-depth interviews were conducted with practicing and student designers from different disciplines who use commercial generative design tools, detailing the design processes they followed. From a grounded theory-based analysis of the interviews, a provisional process diagram for generative design and its uses in the early-stage design process is proposed. The early stages of defining tool inputs bring about a constraint-driven process in which designers focus on the abstraction of the design problem. Designers will iterate through the inputs to improve both quantitative and qualitative metrics. The learning through iteration allows designers to gain a thorough understanding of the design problem and solution space. This can bring about creative applications of generative design tools in early-stage design to provide guidance for traditionally designed products.

    

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5. Evaluating Heuristics in Engineering Design: A Reinforcement Learning Approach

   https://doi.org/10.1115/detc2021-70425  

   ElSayed, Karim A. ; Bilionis, Ilias ; Panchal, Jitesh H. ( August 2021 , ASME IDETC)

   Heuristics are essential for addressing the complexities of engineering design processes. The goodness of heuristics is context-dependent. Appropriately tailored heuristics can enable designers to find good solutions efficiently, and inappropriate heuristics can result in cognitive biases and inferior design outcomes. While there have been several efforts at understanding which heuristics are used by designers, there is a lack of normative understanding about when different heuristics are suitable. Towards addressing this gap, this paper presents a reinforcement learning-based approach to evaluate the goodness of heuristics for three sub-problems commonly faced by designers: (1) learning the map between the design space and the performance space, (2) acquiring sequential information, and (3) stopping the information acquisition process. Using a multi-armed bandit formulation and simulation studies, we learn the suitable heuristics for these individual sub-problems under different resource constraints and problem complexities. Additionally, we learn the optimal heuristics for the combined problem (i.e., the one composing all three sub-problems), and we compare them to ones learned at the sub-problem level. The results of our simulation study indicate that the proposed reinforcement learning-based approach can be effective for determining the quality of heuristics for different problems, and how the effectiveness of the heuristics changes as a function of the designer’s preference (e.g., performance versus cost), the complexity of the problem, and the resources available.

    

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