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.

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.

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.

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.

In this article, we introduce the Special Issue, Value Sensitive Design: Charting the Next Decade, which arose from a week-long workshop hosted by Lorentz Center, Leiden, The Netherlands, November 14–18, 2016. Forty-one researchers and designers, ranging in seniority from doctoral students to full professors, from Australia, Europe, and North America, and representing a wide range of academic fields participated in the workshop. The first article in the special issue puts forward eight grand challenges for value sensitive design to help guide and shape the field. It is followed by 16 articles consisting of value sensitive design nuggets—short pieces of writing on a new idea, method, challenge, application, or other concept that engages some aspect of value sensitive design. The nuggets are grouped into three clusters: theory, method, and applications. Taken together the grand challenges and nuggets point the way forward for value sensitive design into the next decade and beyond.