Systematically integrate moral values into technology design and engineering Translating abstract moral values into concrete design requirements remains a persistent challenge across engineering disciplines. Design For Values: Aims, Methods and Applications provides the first book-length introduction to this proactive approach, presenting a systematic methodology for embedding public and moral values into new technology. Grounded in thirty years of research at leading technical universities, it moves ethics from theory into actionable engineering practice. The book introduces value hierarchies that convert broad principles like sustainability and justice into context-specific design requirements. It provides frameworks for navigating moral overload, value conflict, and stakeholder disagreement through structured design approaches. Four extensive case studies illustrate how these methods work in practice, demonstrating translation from ethical analysis to technical specification across varied domains. Readers will also find: A design perspective on practical ethics that reframes moral reasoning as an integral component of the engineering and innovation process Methods for identifying, prioritizing, and reconciling competing values when multiple stakeholders hold fundamentally different moral positions on technology Concrete examples and illustrations throughout each chapter showing how abstract ethical principles become measurable, verifiable design requirements Coverage spanning digital and physical technologies, offering broader applicability than approaches limited exclusively to ICT systems or software design A concise treatment specifically structured for use in master-level courses, PhD seminars, and professional development programs for practicing engineers Designed for graduate students in engineering, design, and ethics of technology, as well as practicing engineers, designers, managers, and policy makers working in science and technology, this book provides the systematic methodology needed to embed values from sustainability to justice into the technologies shaping society.

Responsible clinical AI requires regionally anchored networks tailored to local contexts. We propose a primary-care-first approach to prioritize local governance, ensuring decisions are context-driven; data sovereignty, to build trust and protect privacy; and community-driven infrastructure, ensuring sustainability. With capacity building, these networks support equitable AI by aligning with local needs, overcoming barriers to implementation, and enabling collaboration across regions to scale solutions effectively.

The European Commission defines the Green Transition as the transformation set out in the European Green Deal. Ensuring that this transition is just is both an ethical requirement and a practical condition for maintaining public support and policy effectiveness. This Perspective proposes a multidimensional framework for assessing justice in Green Transition policies, encompassing distributional, procedural, recognitional, corrective, and transitional dimensions. Considering these dimensions in conjunction helps identify where justice claims converge and where genuine policy trade-offs arise, which should be made transparent and addressed through public deliberation. It sheds light on additional justice considerations which tend to get overlooked in many policy debates that focus predominantly on distributional justice concerns. Moreover, its multidimensionality is helpful in overcoming zero-sum framings which often present impediments for embedding justice throughout the policy cycle.

The advances in machine learning (ML)-based systems in medicine give rise to pressing epistemological and ethical questions. Clinical decisions are increasingly taken in highly digitised work environments, which we call artificial epistemic niches. By considering the case of ML systems in life-critical healthcare settings, we investigate (1) when users’ reliance on these systems can be characterised as epistemic dependence and (2) how this dependence turns into what we refer to as harmful epistemic dependence of clinical professionals on medical ML. The latter occurs when the impossibility of critically assessing the soundness of a system’s output in situ implies a moral obligation to comply with its recommendation since a failure to do so constitutes a moral risk that cannot be justified then and there. We analyse the epistemic and moral consequences of harmful epistemic dependence on the status of medical professionals. We conclude by assessing how a suitable design of the epistemic niche can address the problem.

AI holds the potential to transform healthcare, promising improvements in patient care. Yet, realizing this potential is hampered by over-reliance on limited datasets and a lack of transparency in validation processes. To overcome these obstacles, we advocate the creation of a detailed registry for AI algorithms. This registry would document the development, training, and validation of AI models, ensuring scientific integrity and transparency. Additionally, it would serve as a platform for peer review and ethical oversight. By bridging the gap between scientific validation and regulatory approval, such as by the FDA, we aim to enhance the integrity and trustworthiness of AI applications in healthcare.

This handbook presents the concept of ‘meaningful human control’ (MHC) over AI systems from the perspectives of (i) philosophy and ethics, (ii) law and governance, and (iii) design and engineering. The introductory chapter addresses the motivations and recent developments in MHC, introducing each perspective and related chapters. These three disciplinary perspectives scrutinize how MHC intertwines with philosophical debates on moral responsibility, societal concerns regarding control over technological advancements in legal frameworks, and the engineering complexities of designing and developing AI systems while ensuring human control and responsibility. Additionally, cross-cutting aspects on MHC over AI systems are also introduced and discussed through (iv) interdisciplinary and systemic perspectives. By offering a contextualized introduction to the perspectives considered in this handbook, this chapter aims to present the handbook’s various approaches and points of interest for a diverse audience, highlighting potential entry points into this multidisciplinary volume.

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.