Project objectives Radicalisation research leads to ethical and legal questions and issues. These issues need to be addressed in way that helps the project progress in ethically and legally acceptable manner. Description of Work The legal analysis in SAFIRE addressed questions such as which behavior associated with radicalisation is criminal behaviour. The ethical issues were addressed throughout the project in close cooperation between the ethicists and the researchers using a method called ethical parallel research. Results A legal analysis was made about criminal law and radicalisation. During the project lively discussions were held in the research team about ethical issues. An ethical justification for interventions in radicalisation processes has been written. With regard to research ethics: An indirect informed consent procedure for interviews with (former) radicals has been designed. Practical guidelines to prevent obtaining information that could lead to indirect identification of respondents were developed.
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The development of an integrated ethical or normative model to design housing for persons with impairments and diseases, focusing on older persons with dementia.
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Symposium ESWRA - ECSWR 2016: empirical ethics in social work. Objective: ethical aspects of social work (esp. at home) Structure: cooperation of the research group of UAS Utrecht Netherlands with six regional welfare organizations Method: practice based ethics research Focus on professional practice: learning from moral experiences in frontline practice (cf. Van Doorn, 2008) Hybrid approach: combining theoretical resources and professional practice (cf. Banks & Gallagher, 2009) Mixed methods: desk research, interviews, best practice units (BPU), development of ethical tools
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Youth care is under increasing pressure, with rising demand, longer waiting lists, and growing staff shortages. In the Netherlands, one in seven children and adolescents is currently receiving youth care. At the same time, professionals face high workloads, burnout risks, and significant administrative burdens. This combination threatens both the accessibility and quality of care, leading to escalating problems for young people and families. Artificial intelligence (AI) offers promising opportunities to relieve these pressures by supporting professionals in their daily work. However, many AI initiatives in youth care fail to move beyond pilot stages, due to barriers such as lack of user acceptance, ethical concerns, limited professional ownership, and insufficient integration into daily practice. Empirical research on how AI can be responsibly and sustainably embedded in youth care is still scarce. This PD project aims to develop practice-based insights and strategies that strengthen the acceptance and long-term adoption of AI in youth care, in ways that support professional practice and contribute to appropriate care. The focus lies not on the technology itself, but on how professionals can work with AI within complex, high-pressure contexts. The research follows a cyclical, participatory approach, combining three complementary implementation frameworks: the Implementation Guide (Kaptein), the CFIR model (Damschroder), and the NASSS-CAT framework (Greenhalgh). Three case studies serve as core learning environments: (1) a speech-to-text AI tool to support clinical documentation, (2) Microsoft Copilot 365 for organization-wide adoption in support teams, and (3) an AI chatbot for parents in high-conflict divorces. Throughout the project, professionals, clients, ethical experts, and organizational stakeholders collaborate to explore the practical, ethical, and organizational conditions under which AI can responsibly strengthen youth care services.
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.
