Digital identities are often discussed or explained as digital versions of physical documents such as passports. This metaphor tends to ignore, intentionally or not, the social challenges associated with real-world implementation of these technologies. This paper presents eight alternative metaphors for “good" digital identities which are derived from a 12-month Research-through-Design process. This process is presented as an annotated portfolio showcasing insights from a variety of design activities and stakeholder engagements, including design sprints, workshops, an artist residency and an exhibition, with the metaphors operating as “meta-annotations" on the portfolio. The eight metaphors intend to provoke and enable wider conversation with various stakeholders including academics, non-profits, industry professionals and policy makers about what “good" digital identities might mean, by focusing on societal rather than common technical concerns.

People’s data practices and their supporting personal informatics are imbued with a wide range of concepts of value that are central to their data imaginaries. They inform how individuals adopt particular products and services. This elusiveness of value creates a challenge for designers to gauge users’ value preferences, which are personal, contextual and abstract. We propose deliberation as a process to operationalise design for elicitation, reflection and value formation, using Live Action Role Play (LARP). The paper demonstrates how our deliberative LARP precipitates the otherwise ethereal data values to unfold in a social setting. We argue that engaging people in reflecting on and playing out their own value formation offers designers more powerful insights to address complex challenges of data-driven societies. We report on our findings that agency and negotiability are key to transcend the politics of preferences in data practices and thus enhance acceptability.

Connected devices present new opportunities to advance design through data collection in the wild, similar to the way digital services evolve through analytics. However, it is still unclear how live data transmitted by connected devices informs the design of these products, going beyond performance optimisation to support creative practices. Design can be enriched by data captured by connected devices, from usage logs to environmental sensors, and data about the devices and people around them. Through a series of workshops, this paper contributes industry and academia perspectives on the future of data-driven product design. We highlight HCI challenges, issues and implications, including sensemaking and the generation of design insight. We further challenge current notions of data-driven design and envision ways in which future HCI research can develop ways to work with data in the design process in a connected, rich, human manner.

Shape optimization techniques are becoming increasingly important in design and engineering. This growing significance reflects the need to exploit advances in digital fabrication technologies, and the desire to create new types of surface designs for various engineering applications. Evolutionary algorithms (EAs) offer several key advantages for shape optimization, but they can also be restricted, especially as design problems scale up in size. A key challenge for evolutionary shape optimization is to overcome these challenges in order to apply EAs to large-scale, “real-world” engineering problems. This paper presents a new evolutionary approach to shape optimization using what we call “surface-mapped compositional pattern producing networks (CPPNs).” Our method outperforms a state-of-the-art gradient-based method on a simple benchmark problem, and scales well as degrees of freedom are added to the design problem. Our results demonstrate that surface-mapped CPPNs offer practical ways of approaching large-scale, real-world engineering problems with EAs, opening up exciting new opportunities for engineering design.

Recent advances in fabrication technologies open up exciting opportunities to manufacture entirely new types of physical materials and structures. These have varied and specific mechanical properties, which can be exploited in a number of engineering applications, and a growing area of research concerns the generation of two- and three-dimensional designs using these materials. However, the computational tools required to explore large spaces of possible 2-D and 3-D morphologies remain underdeveloped. State-of-the-art evolutionary approaches such as CPPN-NEAT and HyperNEAT-LEO are often used to explore possible 2-D and 3-D designs, but their ability to construct efficient solutions for practical use in engineering domains remains in question. In this paper, we present an extension of CPPN-NEAT, in which nodes grow connections across a dynamic substrate, and illustrate this by creating efficient 2-D truss structures. Using four benchmark problems, we then demonstrate that our extended CPPN-NEAT model outperforms similar HyperNEAT methods for approximating specific connectivity patterns, and suggests important clues regarding how to best harness generative and developmental representations to build scalable and high-performance physical morphologies.

We present a developmental genotype-phenotype growth process, or embryogeny, which is used to evolve, in silico, efficient three-dimensional structures that exhibit real-world architectural performance. The embryogeny defines a sequential assembly of architectural components within a three-dimensional volume, and indirectly establishes a regulatory network of components based on the principles of gene regulation. The implicitly regulated phenotypes suggest advances for the automatic design of physical structures, by improving scalability of the genotype encoding and embedding real-world constraints. We demonstrate that our model can evolve novel, yet efficient, architectural structures which exhibit emergent shape, topology and material distribution. Finally, we compare evolved structures against a "hand-coded" solution to illustrate that our model produces competitive results without prior knowledge of the design solution or direct human guidance.