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Editorial
Design for and with values in designing for social change (Design Ethics SIG)
Journal article
(2026)
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Deger Ozkaramanli, F. Abou Eddahab-Burke, S Celik, G. Slingerland, Michael Nagenborg, Natalia Gulbransen-Diaz, Leigh Anne Hepburn
This editorial introduces the theme track ‘Design for and with values in designing for social change’. Building on prior work that frame ethics as an invitation to care, the track focuse on values as a central construct in socially engaged design. It responds to a growing need to move beyond abstract value frameworks toward understanding how values are elicited, surfaced, negotiated, and enacted in practice. Drawing on 24 selected papers, we reveal a strongly interdisciplinary collection where participatory design emerges as a dominant method. The contributions are organized into four overlapping themes revolving around the concepts of power, methods, dignity and care. The track highlights both instrumental and critical approaches as well as top-down and bottom-up value articulations. With this, it emphasizes the importance of context, relationality, and reflexivity in socially engaged design.
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This editorial introduces the theme track ‘Design for and with values in designing for social change’. Building on prior work that frame ethics as an invitation to care, the track focuse on values as a central construct in socially engaged design. It responds to a growing need to move beyond abstract value frameworks toward understanding how values are elicited, surfaced, negotiated, and enacted in practice. Drawing on 24 selected papers, we reveal a strongly interdisciplinary collection where participatory design emerges as a dominant method. The contributions are organized into four overlapping themes revolving around the concepts of power, methods, dignity and care. The track highlights both instrumental and critical approaches as well as top-down and bottom-up value articulations. With this, it emphasizes the importance of context, relationality, and reflexivity in socially engaged design.
Pre-operative anatomical data is essential for guidance in neurosurgery, but brain movement and tissue manipulation degrade its registration to the patient during surgery. Restoring the registration requires intra-operative updates of the surface geometry, which a calibrated stereo surgical microscope could triangulate from the rectified image pairs it delivers. However, low-texture surfaces, reflections, and blur make stereo matching in this domain difficult. Zero-shot stereo foundation models, deep networks trained on large synthetic datasets of natural images, generalize to unseen domains without fine-tuning and handle comparable conditions well on public benchmarks. Yet their performance for neurosurgery is unknown because no benchmark exists to evaluate them in this domain.
We close this gap by assessing whether zero-shot stereo foundation models are suitable for stereo reconstruction in neurosurgical microscopy by analyzing their accuracy, robustness, and failure predictability. To this end, the Microscopy Images for Neurosurgical Dense Stereo reconstruction (MINDS) benchmark is introduced, which provides realistic synthetic and ex-vivo porcine brain images with reference depth and segmentation masks. Four zero-shot models and two classical reference methods are evaluated on MINDS for reconstruction accuracy and robustness under reflections, blur, and weak texture. Additionally, a failure predictor is developed from inference-time confidence cues to investigate whether unreliable depth estimates could be caught in practice.
The best classical method places 23% and 76% of pixels within 0.1 mm and 1 mm of the reference, respectively. The best zero-shot model reaches 59% and 97% at the same thresholds. This accuracy gap persists across all texture levels. Under specular reflection, the best zero-shot model stays at 55% and 99%, while the reference methods drop to 4% and 42%. Defocus blur, by contrast, degrades every tested method to a similar extent and remains the dominant barrier to sub-millimeter accuracy. The failure predictor separates reliable from unreliable pixels at an average precision of 0.63 on synthetic data, nearly four times the chance level of 0.17, with recall rising from 0.75 for errors of 1 mm to 5 mm to 0.99 for errors above 10 mm. Together, these results establish the present capabilities and limitations of zero-shot foundation models for neurosurgical stereo reconstruction.
...
We close this gap by assessing whether zero-shot stereo foundation models are suitable for stereo reconstruction in neurosurgical microscopy by analyzing their accuracy, robustness, and failure predictability. To this end, the Microscopy Images for Neurosurgical Dense Stereo reconstruction (MINDS) benchmark is introduced, which provides realistic synthetic and ex-vivo porcine brain images with reference depth and segmentation masks. Four zero-shot models and two classical reference methods are evaluated on MINDS for reconstruction accuracy and robustness under reflections, blur, and weak texture. Additionally, a failure predictor is developed from inference-time confidence cues to investigate whether unreliable depth estimates could be caught in practice.
The best classical method places 23% and 76% of pixels within 0.1 mm and 1 mm of the reference, respectively. The best zero-shot model reaches 59% and 97% at the same thresholds. This accuracy gap persists across all texture levels. Under specular reflection, the best zero-shot model stays at 55% and 99%, while the reference methods drop to 4% and 42%. Defocus blur, by contrast, degrades every tested method to a similar extent and remains the dominant barrier to sub-millimeter accuracy. The failure predictor separates reliable from unreliable pixels at an average precision of 0.63 on synthetic data, nearly four times the chance level of 0.17, with recall rising from 0.75 for errors of 1 mm to 5 mm to 0.99 for errors above 10 mm. Together, these results establish the present capabilities and limitations of zero-shot foundation models for neurosurgical stereo reconstruction.
...
Pre-operative anatomical data is essential for guidance in neurosurgery, but brain movement and tissue manipulation degrade its registration to the patient during surgery. Restoring the registration requires intra-operative updates of the surface geometry, which a calibrated stereo surgical microscope could triangulate from the rectified image pairs it delivers. However, low-texture surfaces, reflections, and blur make stereo matching in this domain difficult. Zero-shot stereo foundation models, deep networks trained on large synthetic datasets of natural images, generalize to unseen domains without fine-tuning and handle comparable conditions well on public benchmarks. Yet their performance for neurosurgery is unknown because no benchmark exists to evaluate them in this domain.
We close this gap by assessing whether zero-shot stereo foundation models are suitable for stereo reconstruction in neurosurgical microscopy by analyzing their accuracy, robustness, and failure predictability. To this end, the Microscopy Images for Neurosurgical Dense Stereo reconstruction (MINDS) benchmark is introduced, which provides realistic synthetic and ex-vivo porcine brain images with reference depth and segmentation masks. Four zero-shot models and two classical reference methods are evaluated on MINDS for reconstruction accuracy and robustness under reflections, blur, and weak texture. Additionally, a failure predictor is developed from inference-time confidence cues to investigate whether unreliable depth estimates could be caught in practice.
The best classical method places 23% and 76% of pixels within 0.1 mm and 1 mm of the reference, respectively. The best zero-shot model reaches 59% and 97% at the same thresholds. This accuracy gap persists across all texture levels. Under specular reflection, the best zero-shot model stays at 55% and 99%, while the reference methods drop to 4% and 42%. Defocus blur, by contrast, degrades every tested method to a similar extent and remains the dominant barrier to sub-millimeter accuracy. The failure predictor separates reliable from unreliable pixels at an average precision of 0.63 on synthetic data, nearly four times the chance level of 0.17, with recall rising from 0.75 for errors of 1 mm to 5 mm to 0.99 for errors above 10 mm. Together, these results establish the present capabilities and limitations of zero-shot foundation models for neurosurgical stereo reconstruction.
We close this gap by assessing whether zero-shot stereo foundation models are suitable for stereo reconstruction in neurosurgical microscopy by analyzing their accuracy, robustness, and failure predictability. To this end, the Microscopy Images for Neurosurgical Dense Stereo reconstruction (MINDS) benchmark is introduced, which provides realistic synthetic and ex-vivo porcine brain images with reference depth and segmentation masks. Four zero-shot models and two classical reference methods are evaluated on MINDS for reconstruction accuracy and robustness under reflections, blur, and weak texture. Additionally, a failure predictor is developed from inference-time confidence cues to investigate whether unreliable depth estimates could be caught in practice.
The best classical method places 23% and 76% of pixels within 0.1 mm and 1 mm of the reference, respectively. The best zero-shot model reaches 59% and 97% at the same thresholds. This accuracy gap persists across all texture levels. Under specular reflection, the best zero-shot model stays at 55% and 99%, while the reference methods drop to 4% and 42%. Defocus blur, by contrast, degrades every tested method to a similar extent and remains the dominant barrier to sub-millimeter accuracy. The failure predictor separates reliable from unreliable pixels at an average precision of 0.63 on synthetic data, nearly four times the chance level of 0.17, with recall rising from 0.75 for errors of 1 mm to 5 mm to 0.99 for errors above 10 mm. Together, these results establish the present capabilities and limitations of zero-shot foundation models for neurosurgical stereo reconstruction.
T4G
Trace-based P4 Program Generation
While the adoption of programmable data planes is increasing, writing correct and efficient P4 programs remains a challenging and error-prone process. This hinders the rapid development of custom in-network functionality and limits the accessibility of data-plane programming to a small set of experts. In this paper, we present T4G, a system that automatically synthesizes P4 programs from network behavior expressed as input-output network traces. Unlike prior approaches that rely on high-level abstraction or domain-specific languages, T4G operates with example packet captures, eliminating the need for manual abstraction or rule specifications from network operators. T4G introduces a novel input-output grouping technique that identifies logical packet transformation behavior from packet traces, and performs a divide-and-conquer synthesis that scales to complex forwarding logic. Our experimental results show the ability of T4G to generate different types of programs and demonstrate the potential of T4G in generating complex P4 programs.
...
While the adoption of programmable data planes is increasing, writing correct and efficient P4 programs remains a challenging and error-prone process. This hinders the rapid development of custom in-network functionality and limits the accessibility of data-plane programming to a small set of experts. In this paper, we present T4G, a system that automatically synthesizes P4 programs from network behavior expressed as input-output network traces. Unlike prior approaches that rely on high-level abstraction or domain-specific languages, T4G operates with example packet captures, eliminating the need for manual abstraction or rule specifications from network operators. T4G introduces a novel input-output grouping technique that identifies logical packet transformation behavior from packet traces, and performs a divide-and-conquer synthesis that scales to complex forwarding logic. Our experimental results show the ability of T4G to generate different types of programs and demonstrate the potential of T4G in generating complex P4 programs.
The Greenland and Antarctic ice sheets are major drivers of global mean sea level rise and are predicted to continue to do so in the future. However, they also represent the largest source of uncertainty in projections of future sea level rise making robust estimates of observed ice sheet mass changes critical. Here, we compare and combine 42 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, volume, and gravitational attraction to determine the ice sheet mass balance from 1972 (Greenland) and 1979 (Antarctica) until 2023. We then use regional climate models to partition the total mass balance into contributions associated with surface mass balance and ice dynamical imbalance. The ice sheets lost 11,309 ± 565 billion tonnes of ice between 1979 and 2023, with glacier dynamical imbalance driving 84% of the ice loss and surface mass balance the remainder. This dataset can be used to track the contribution of the ice sheets to sea level rise and constrain projections of future sea level rise.
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The Greenland and Antarctic ice sheets are major drivers of global mean sea level rise and are predicted to continue to do so in the future. However, they also represent the largest source of uncertainty in projections of future sea level rise making robust estimates of observed ice sheet mass changes critical. Here, we compare and combine 42 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, volume, and gravitational attraction to determine the ice sheet mass balance from 1972 (Greenland) and 1979 (Antarctica) until 2023. We then use regional climate models to partition the total mass balance into contributions associated with surface mass balance and ice dynamical imbalance. The ice sheets lost 11,309 ± 565 billion tonnes of ice between 1979 and 2023, with glacier dynamical imbalance driving 84% of the ice loss and surface mass balance the remainder. This dataset can be used to track the contribution of the ice sheets to sea level rise and constrain projections of future sea level rise.
A Collaborative Online International Learning program (COIL) is a virtual exchange model in which students from different universities use digital tools to get an international and intercultural experience. To provide such an experience to undergraduate civil engineering students from Ecuador (USFQ) and Chile (PUCV), a COIL module consisting of 13 shared sessions of lectures and three main activities (icebreaker, design project, final reflection) was designed. This paper reports on the curricular design of a COIL for bridge design teaching, the findings from the activities regarding code comparison and software usage, the joint midterm exam, the reflections of the students, the survey results, and the effect on the overall course evaluation. These insights can serve other bridge engineering instructors to develop similar virtual exchanges for their students, and add to the reflections on innovation in bridge engineering education, the role of digitalization, the development of intercultural competences, and teacher–student reflection.
...
A Collaborative Online International Learning program (COIL) is a virtual exchange model in which students from different universities use digital tools to get an international and intercultural experience. To provide such an experience to undergraduate civil engineering students from Ecuador (USFQ) and Chile (PUCV), a COIL module consisting of 13 shared sessions of lectures and three main activities (icebreaker, design project, final reflection) was designed. This paper reports on the curricular design of a COIL for bridge design teaching, the findings from the activities regarding code comparison and software usage, the joint midterm exam, the reflections of the students, the survey results, and the effect on the overall course evaluation. These insights can serve other bridge engineering instructors to develop similar virtual exchanges for their students, and add to the reflections on innovation in bridge engineering education, the role of digitalization, the development of intercultural competences, and teacher–student reflection.