This chapter provides insight into the culturally-bound nature of ethical sensitivity by examining three case studies from different educational contexts: the Netherlands (n = 622), Finland (n = 864), and Iranian Kurdistan (n = 556). Ethical sensitivity was investigated with the Ethical Sensitivity Scale Questionnaire (Tirri & Nokelainen, 2007, 2011), and a four-factor model was found to capture the essential aspects of ethical sensitivity across culturally diverse contexts. Subsequently, the relationships among the four dimensions were examined in each case study. The analyses reveal that caring by connecting to others was a central dimension of ethical sensitivity across the three cases. Given the other dimensions of ethical sensitivity, the dimension of taking the perspective of others seemed particularly dependent on culture. The consequences of these results for moral education are discussed.
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Higher educational institutions incorporate projects into their curricula, in which students, together with educators, researchers and professionals from practice, try to find solutions for real, societal problems, to develop relevant skills. Because such solutions are increasingly digital with high impact on society, ethical responsibility is an important part of these skills. In this study, we analyze two cases of digital innovation projects in higher education in which the concept of the Ethical Matrix is adapted and integrated in a Value Sensitive Design approach and applied by educators (case 1) and by students (case 2). We find that an adapted version of the Ethical Matrix supports educators and students in taking values of different types of stakeholders into account which leads to different design choices.
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This paper argues online privacy controls are based on a transactional model of privacy, leading to a collective myth of consensual data practices. It proposes an alternative based on the notion of privacy coordination as an alternative vision and realizing this vision as a grand challenge in Ethical UX
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
Entangled Machines is a project by Mariana Fernández Mora that interrogates the colonial and extractive legacies underpinning artificial intelligence (AI). By introducing slowness and digital kinship as critical frameworks, the project reconceptualises AI as embedded within intricate social and ecological networks, thereby contesting dominant narratives of efficiency and optimisation. Through participatory, practice-based methodologies such as the Material Playground, the project integrates feminist and non-Western epistemologies to articulate alternative models for ethical, sustainable, and equitable AI practices. Over a four-year period, Entangled Machines develops theory, engages diverse communities, and produces artistic outputs to reimagine human-AI interactions. In collaboration with partners including ARIAS Amsterdam, Archival Consciousness, and the Sandberg Institute, the research seeks to foster decolonial and interdisciplinary approaches to AI. Its culmination will be an “Anarchive” – a curated assemblage of artistic, theoretical, and archival outputs – that serves as a resource for rethinking AI’s socio-political and ecological impacts.
CRISPR/Cas genome engineering unleashed a scientific revolution, but entails socio-ethical dilemmas as genetic changes might affect evolution and objections exist against genetically modified organisms. CRISPR-mediated epigenetic editing offers an alternative to reprogram gene functioning long-term, without changing the genetic sequence. Although preclinical studies indicate effective gene expression modulation, long-term effects are unpredictable. This limited understanding of epigenetics and transcription dynamics hampers straightforward applications and prevents full exploitation of epigenetic editing in biotechnological and health/medical applications.Epi-Guide-Edit will analyse existing and newly-generated screening data to predict long-term responsiveness to epigenetic editing (cancer cells, plant protoplasts). Robust rules to achieve long-term epigenetic reprogramming will be distilled based on i) responsiveness to various epigenetic effector domains targeting selected genes, ii) (epi)genetic/chromatin composition before/after editing, and iii) transcription dynamics. Sustained reprogramming will be examined in complex systems (2/3D fibroblast/immune/cancer co-cultures; tomato plants), providing insights for improving tumor/immune responses, skin care or crop breeding. The iterative optimisations of Epi-Guide-Edit rules to non-genetically reprogram eventually any gene of interest will enable exploitation of gene regulation in diverse biological models addressing major societal challenges.The optimally balanced consortium of (applied) universities, ethical and industrial experts facilitates timely socioeconomic impact. Specifically, the developed knowledge/tools will be shared with a wide-spectrum of students/teachers ensuring training of next-generation professionals. Epi-Guide-Edit will thus result in widely applicable effective epigenetic editing tools, whilst training next-generation scientists, and guiding public acceptance.
