Conrad S. Tucker

Design is how humans change the world, and in the age of AI it is increasingly a joint activity between humans and machines. This perspective argues that AI does not simply add new tools to the designer’s repertoire; it has the capability to reorganize the division of labor in design and, in doing so, reshape what it means to be a designer. We distinguish between representative technologies, which model and derisk complex systems (eg digital twins, immersive simulations), and operative technologies, which act directly within design processes (eg generative and agentic systems that propose, evaluate, and select alternatives). Viewed through the Five-Cycle model of design, we argue that these technologies widen inputs, accelerate exploration, and tighten feedback across problem definition, conceptual and embodiment design, and value proposition. In our model, representative tools derisk what to believe; operative tools derisk what to try. Together these technologies could enable closed-loop, hybrid human-AI design processes in which human roles shift from manual problem solving toward stewardship, curation, translation, and, in a democratized future, even historical guardianship of designerly knowledge. We contend that design science is essential for understanding and guiding this transition: explaining how human purpose, creativity, and responsibility are redistributed in human-AI teams; developing methods to study how values propagate through automated design workflows; and informing education and practice so that increasingly automated design processes remain aligned with human intent and societal well-being.

Video-based physiological signal estimation is crucial for non-invasive and contactless patient monitoring in scenarios where traditional sensor-based methods are impractical, such as telemedicine and large-scale population health studies. The development and efficacy of such approaches is critically influenced by the availability of representative human subject datasets that reflect real-world scenarios. However, existing datasets predominantly consist of recordings collected in controlled laboratory settings with limited diversity in environmental conditions and demographic representation. Such constraints hinder the generalizability and robustness of video-based physiological signal estimation approaches in real-world scenarios (due to real-world scenarios demonstrating a challenging distribution of environmental diversity as compared to laboratory conditions). To this end, we present the REmote VITals (REVIT) dataset, comprising of 1553 human subject videos and associated ground truth physiological sign measurements (such as heart rate) collected in multiple outside-the-lab settings for the remote estimation of human physiological signs. The dataset is uniquely designed to capture a wide range of environmental diversity (such as variable lighting and background) encountered in outside-the-lab settings, thereby enabling the development of more robust physiological signal estimation approaches. Through statistical hypothesis testing, we demonstrate that utilizing the REVIT dataset results in a significant improvement in the robustness of existing video-based physiological signal estimation approaches. Furthermore, this work highlights the impact of existing data distributions toward controlled laboratory conditions, underscoring their limitations in real world applicability.

Data sovereignty in Africa is challenged by fragmented data protection laws, limited cross-border frameworks, and infrastructural deficits. While over 40 countries have enacted data protection laws, inconsistent implementation and over ten nations without legislation hinder continental coherence, especially for personal data. Non-personal data remains largely unregulated. Efforts like the AfCFTA Digital Trade Protocol and the AU Data Policy Framework have yet to achieve harmonized data transfer mechanisms. Infrastructural issues, particularly unreliable power, further undermine data localization efforts. Against this backdrop, the concept of data embassies, drawing on Estonia’s model anchored in the Vienna Convention and reinforced by bilateral agreements, presents a potential alternative. This position paper examines the legal and technical viability of such models within Africa. Through comparative legal and policy analysis, it explores how data embassies could support African states in enhancing digital sovereignty while addressing existing regulatory and infrastructural constraints.

We identify two significant issues that render prosthetics inaccessible. First, obtaining a representation of the residual limb can be inaccessible. Conventional approaches require equipment or expertise often unavailable in resource-constrained communities. Second, it is challenging to determine the prosthetic design, filament material, and printing process that satisfies mechanical functionality requirements because it is difficult to predict the mechanical properties of 3D-printed prosthetics. Therefore, we propose a method to achieve a digital residual limb model from a smartphone video and predict the mechanical functionality of the 3D-printed prosthetic. We also present a case study that demonstrates the feasibility of the method. Digital reconstruction results show that the smartphone type influences reconstruction time and mesh quality, with correlation coefficients of 0.89 and 0.88, respectively. Also, the distance between the residual limb and the smartphone influences the reconstruction scale, with a correlation coefficient of –0.90. Seven of eight digital reconstruction results achieved an average deviation lower than 2 mm, which is viable for designing prosthetics. The XGBoost model trained to predict the effective material data of the 3D-printed part achieved an R2 over 0.99 for all predictions. The predicted effective material data are used to predict the mechanical functionality of a 3D-printed prosthetic. The mechanical functionality is evaluated following ISO-10328. The results reveal that different prosthetic designs, filament materials, and printing processes yield different mechanical functionality. These factors can be determined according to the predicted functionalities and the patient’s needs.

Addressing the uncertainty and variability in the quality of 3D printed metals can further the wide spread use of this technology. Process mapping for new alloys is crucial for determining optimal process parameters that consistently produce acceptable printing quality. Process mapping is typically performed by conventional methods and is used for the design of experiments and ex situ characterization of printed parts. On the other hand, in situ approaches are limited because their observable features are limited and they require complex high-cost setups to obtain temperature measurements to boost accuracy. Our method relaxes these limitations by incorporating the temporal features of molten metal dynamics during laser-metal interactions using video vision transformers and high-speed imaging. Our approach can be used in existing commercial machines and can provide in situ process maps for efficient defect and variability quantification. The generalizability of the approach is demonstrated by performing cross-dataset evaluations on alloys with different compositions and intrinsic thermofluid properties.

In this work, we propose a non-contact video-based approach that estimates an individual’s blood pressure. The estimation of blood pressure is critical for monitoring hypertension and cardiovascular diseases such as coronary artery disease or stroke. Estimation of blood pressure is typically achieved using contact-based devices which apply pressure on the arm through a cuff. Such contact-based devices are cost-prohibitive as well as limited in their scalability due to the requirement of specialized equipment. The ubiquity of mobile phones and video-based capturing devices motivates the development of a non-contact blood pressure estimation method - Video-based Blood Pressure Estimation (V-BPE). We leverage the time difference of the blood pulse arrival at two different locations in the body (Pulse Transit Time) and the inverse relation between the blood pressure and the velocity of blood in the artery to analytically estimate the blood pressure. Through statistical hypothesis testing, we demonstrate that Pulse Transit Time-based approaches to estimate blood pressure require knowledge of subject specific blood vessel parameters, such as the length of the blood vessel. We utilize a combination of computer vision techniques and demographic information (such as the height and the weight of the subject) to capture and incorporate the aforementioned subject specific blood vessel parameters into our estimation of blood pressure. We demonstrate the robustness of V-BPEby evaluating the efficacy of blood pressure estimation in demographically diverse, outside-the-lab conditions. V-BPE is advantageous in two ways; 1) it is non-contact-based, reducing the possibility of infection due to contact and 2) it is scalable, given the ubiquity of video recording devices.