This paper examines ethical, legal, and social issues (ELSI) associated with healthcare digital twins (DTs). Using a systematic thematic analysis, we identify key themes across ethical, legal, and social categories, as well as practical barriers to adoption and implementation. Findings reveal a range of concerns (e.g. lack of empirical validation of DT value propositions) and gaps between the promise of healthcare DTs and the reality of deploying them within existing healthcare systems. By mapping the identified barriers to the Non-adoption, abandonment, scale-up, spread, and sustainability (NASSS) framework, we seek to demonstrate how healthcare DTs require systematic assurance and capability development. We provide practical recommendations for how this can be achieved, with specified stakeholder groups, to help build trustworthy and ethical DTs with demonstrable healthcare value.

Artificial intelligence (AI) is increasingly being introduced into healthcare to improve efficiency, accuracy, and personalisation. Debate has centred on concerns such as bias, safety, and transparency. We argue that another problem deserves much more attention: misrecognition. By this we mean the risk that digital systems know patients mainly through what is easiest to measure and record, while overlooking what is hardest to code but most central to living with illness. Drawing on the concept of epistemic injustice, we suggest that patients may be disbelieved, misunderstood or required to translate complex, embodied, and relational experiences into clinical categories that fit poorly. Our point is not that patients' accounts fall outside data, but that all data require interpretation, and patient and caregiver inputs are often treated as lower-status knowledge in decisions about burden, benefit, and value. These risks do not arise uniformly across computational tools: they take different forms in task-specific machine learning systems used for classification or prediction, and in generative AI systems, including large language models, used to process or generate text. AI may deepen the problem by relying on proxies such as cost, utilization, and adherence thereby hardening narrow understandings of illness into technical systems. Using pulmonary hypertension (PH) as a case, we reflect on patient-led outcome measures such as emPHasis-10 to show that measurement is never neutral: it shapes what counts as legitimate knowledge, meaningful change, and good care. The key question is not only whether AI is accurate or fair, but what patient experiences become visible within it.

Digital phantoms are virtual representations of the human body used in medical research to test equipment, train medical professionals and develop or validate algorithms. These models can be created from ‘real-world’ clinical data or from ‘synthetic data’. Phantoms derived from clinical data often serves as ‘ground truth’ reference values anchored in empirical observations. However, there is growing demand for synthetic digital phantoms and datasets that do not originate from real patients, raising critical questions about how reliable knowledge is produced from data detached from reality. This article aims to investigate these issues through a document analysis of peer-reviewed publications on the development and use of digital phantoms in medical physics. We examine how researchers construct ‘ground truth’ and the challenges they encounter when advancing truth claims through technical work. By attending to the bodies fabricated in phantom creation and to the data made to represent human form, we show how synthetic data – detached from real human subjects – are valued for enabling researchers to sidestep the complexities or ‘messiness’ of real-world patients and clinical data. Moreover, we show how synthetic phantoms and data are framed as tools that enhance control and flexibility, functioning as ‘known truths’: workable approximations that enables the construction of what are claimed to be more representative datasets and models. This article contributes to Science and Technology Studies and critical data studies by examining the nature and implications of digital representations and synthetic data in the development of machine-learning models in medicine, and the truth claims they support.

Autonomous vehicles (AVs) represent a potential technological transformation of transportation systems, however, incidents involving them have highlighted the complex challenge of assigning liability. While a growing body of literature addresses legal and technical liability, the communication of liability - how legal, moral, or financial responsibility for adverse outcomes is conveyed among stakeholders such as manufacturers, users, insurers and policymakers - remains a critical gap. This multidisciplinary systematic literature review analyzes 90 academic articles published between 2015 and 2024 across a range of disciplines to map the current state of liability communication. Specifically, it examines how liability is communicated: who or what is held accountable for potential harms, under what conditions and through what mechanisms. We find that liability communication is often reactive, inconsistent and poorly aligned with public understanding. Despite the development of expert legal and technical frameworks, communication practices frequently fail to bridge the gap between expert discourse and end-user comprehension. The analysis is organised across five key themes: governance challenges; safety concerns; ownership models; cross-country comparisons; and future AV deployment. Across all five, communication failures are consistently linked to ambiguous terminology and the absence of proactive, standardised protocols. Together, these themes contribute to a more nuanced understanding of how liability is communicated within the evolving AV ecosystem. They also highlight an urgent need for updated policies and more effective, stakeholder-oriented communication strategies. In response, this study offers a necessary reframing of the problem - calling for the development of stakeholder-centric communication practices capable of functioning even amid legal uncertainty. Addressing these challenges is essential not only for effective AV integration but also for ensuring that this transformation unfolds safely and equitably.

This systematic literature review examines AI transparency laws and governance in the European Union (EU) and the United Kingdom (UK) through a socio-legal lens. The study highlights the importance of transparency in AI systems as a key regulatory focus globally, driven by the need to address the risks posed by opaque, ‘black box’ algorithms that can lead to unfair outcomes, privacy violations, and a lack of accountability. It identifies significant differences between the EU and UK approaches to AI regulation post-Brexit, with the EU's tiered, risk-based framework and the UK's more flexible, sector-specific strategy. The review categorises the literature into five themes: the necessity of AI transparency, challenges in achieving transparency, techniques for governing transparency, laws governing AI transparency, and soft law governance toolkits. The findings suggest that while technical solutions like eXplainable AI (XAI) and counterfactual methodologies are widely discussed, there is a critical need for a comprehensive, whole-of-organisation approach to embedding AI transparency within the cultural and operational fabric of organisations. This approach is argued to be more effective than top-down mandates, fostering an internal culture where transparency is valued and sustained. The study concludes by advocating for the development of AI transparency toolkits, particularly for small and medium-sized enterprises (SMEs), to address sociotechnical barriers and ensure that transparency in AI systems is practically implemented across various organisational contexts. These toolkits would serve as practical guides for companies to adopt best practices in AI transparency, aligning with both legal requirements and broader sociocultural considerations.

During the 2019-20 English football season the Football Association, Heads Together mental health charity, and Public Health England, launched the Heads Up campaign to raise fans' awareness about mental health issues. This research examines the campaign's delivery and implementation of well-being resources and messaging from a fan-perspective within a stadium and the shift into a media-mediated campaign during the pandemic. Our methods included ethnographic observation of the campaign in and around a football stadium, and analyses of the campaign's promotional videos, matchday programmes, TV and radio coverage, and social media posts. Our findings reveal positive aspects of the campaign methods, which included the delivery of a football--oriented and coherent set of accessible resources geared towards normalising mental health conversations. However, there were several missed opportunities in delivery linked to limited control over the deployment of -campaign resources and a lack of future planning to build on the campaigns initial impact.

Soft robotics is an emerging technology in which engineers create flexible devices for use in a variety of applications. In order to advance the wide adoption of soft robots, ensuring their trustworthiness is essential; if soft robots are not trusted, they will not be used to their full potential. In order to demonstrate trustworthiness, a specification needs to be formulated to define what is trustworthy. However, even for soft robotic grippers, which is one of the most mature areas in soft robotics, the soft robotics community has so far given very little attention to formulating specifications. In this work, we discuss the importance of developing specifications during development of soft robotic systems, and present an extensive example specification for a soft gripper for pick-and-place tasks for grocery items. The proposed specification covers both functional and non-functional requirements, such as reliability, safety, adaptability, predictability, ethics, and regulations. We also highlight the need to promote verifiability as a first-class objective in the design of a soft gripper.

The past decade has seen efforts to develop new forms of autonomous systems with varying applications in different domains, from underwater search and rescue to clinical diagnosis. All of these applications require risk analyses, but such analyses often focus on technical sources of risk without acknowledging its wider systemic and organizational dimensions. In this article, we illustrate this deficit and a way of redressing it by offering a more systematic analysis of the sociotechnical sources of risk in an autonomous system. To this end, the article explores the development, deployment, and operation of an autonomous robot swarm for use in a public cloakroom in light of Macrae's structural, organizational, technological, epistemic, and cultural framework of sociotechnical risk. We argue that this framework provides a useful tool for capturing the complex "nontechnical" dimensions of risk in this domain that might otherwise be overlooked in the more conventional risk analyses that inform regulation and policymaking.

This report presents a comprehensive response to the United Nation's Interim Report on Governing Artificial Intelligence (AI) for Humanity. It emphasizes the transformative potential of AI in achieving the Sustainable Development Goals (SDGs) while acknowledging the need for robust governance to mitigate associated risks. The response highlights opportunities for promoting equitable, secure, and inclusive AI ecosystems, which should be supported by investments in infrastructure and multi-stakeholder collaborations across jurisdictions. It also underscores challenges, including societal inequalities exacerbated by AI, ethical concerns, and environmental impacts. Recommendations advocate for legally binding norms, transparency, and multi-layered data governance models, alongside fostering AI literacy and capacity-building initiatives. Internationally, the report calls for harmonising AI governance frameworks with established laws, human rights standards, and regulatory approaches. The report concludes with actionable principles for fostering responsible AI governance through collaboration among governments, industry, academia, and civil society, ensuring the development of AI aligns with universal human values and the public good.

The disclosure of absences from professional sporting activities to the media is a routine and generally unproblematic part of a sporting career. However, when the reason for the absence relates to mental health concerns, players can encounter difficulties in trying to define, describe and conceptualise their own issues while attempting to maintain privacy as they undergo assessment and treatment. Drawing on ethnomethodology and conversation analysis principles and methods, this paper explores first/initial public mental health disclosure narratives produced by players and sporting organizations across several professional sports via media interviews, press statements, and social media posts. The analysis focuses on (in)voluntary accounts produced by teams or players themselves during their careers and examines the different communication strategies they employ to categorise and explain their predicament. The analysis reveals how some players provide partial or proxy public disclosure announcements (due to a desire to mask issues or delayed help-seeking and assessment), whereas others prefer fuller disclosure of the problems experienced, including diagnoses and on-going treatment and therapy regimes. The paper outlines the consequences of these disclosure strategies and considers the implications they can have for a player’s wellbeing in these stressful circumstances.

Despite the promise of medical artificial intelligence applications, their acceptance in real-world clinical settings is low, with lack of transparency and trust being barriers that need to be overcome. We discuss the importance of the collaborative process in medical artificial intelligence, whereby experts from various fields work together and tackle transparency issues and build trust over time.

Artificial intelligence (AI) and machine learning (ML) techniques occupy a prominent role in medical research in terms of the innovation and development of new technologies. However, while many perceive AI as a technology of promise and hope-one that is allowing for more early and accurate diagnosis-the acceptance of AI and ML technologies in hospitals remains low. A major reason for this is the lack of transparency associated with these technologies, in particular epistemic transparency, which results in AI disturbing or troubling established knowledge practices in clinical contexts. In this article, we describe the development process of one AI application for a clinical setting. We show how epistemic transparency is negotiated and co-produced in close collaboration between AI developers and clinicians and biomedical scientists, forming the context in which AI is accepted as an epistemic operator. Drawing on qualitative research with collaborative researchers developing an AI technology for the early diagnosis of a rare respiratory disease (pulmonary hypertension/PH), this paper examines how including clinicians and clinical scientists in the collaborative practices of AI developers de-troubles transparency. Our research shows how de-troubling transparency occurs in three dimensions of AI development relating to PH: querying of data sets, building software and training the model The close collaboration results in an AI application that is at once social and technological: it integrates and inscribes into the technology the knowledge processes of the different participants in its development. We suggest that it is a misnomer to call these applications 'artificial' intelligence, and that they would be better developed and implemented if they were reframed as forms of sociotechnical intelligence.

A digital twin is a computer-based "virtual" representation of a complex system, updated using data from the "real" twin. Digital twins are established in product manufacturing, aviation, and infrastructure and are attracting significant attention in medicine. In medicine, digital twins hold great promise to improve prevention of cardiovascular diseases and enable personalised health care through a range of Internet of Things (IoT) devices which collect patient data in real-time. However, the promise of such new technology is often met with many technical, scientific, social, and ethical challenges that need to be overcome-if these challenges are not met, the technology is therefore less likely on balance to be adopted by stakeholders. The purpose of this work is to identify the facilitators and barriers to the implementation of digital twins in cardiovascular medicine. Using, the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, we conducted a document analysis of policy reports, industry websites, online magazines, and academic publications on digital twins in cardiovascular medicine, identifying potential facilitators and barriers to adoption. Our results show key facilitating factors for implementation: preventing cardiovascular disease, in silico simulation and experimentation, and personalised care. Key barriers to implementation included: establishing real-time data exchange, perceived specialist skills required, high demand for patient data, and ethical risks related to privacy and surveillance. Furthermore, the lack of empirical research on the attributes of digital twins by different research groups, the characteristics and behaviour of adopters, and the nature and extent of social, regulatory, economic, and political contexts in the planning and development process of these technologies is perceived as a major hindering factor to future implementation.

Drones, unmanned aircraft controlled remotely and equipped with cameras, have seen widespread deployment across military, industrial, and commercial domains. The commercial sector, in particular, has experienced rapid growth, outpacing regulatory developments due to substantial financial incentives. The UK construction sector exemplifies a case where the regulatory framework for drones remains unclear. This article investigates the state of UK legislation on commercial drone use in construction through a thematic analysis of peer-reviewed literature. Four main themes, including opportunities, safety risks, privacy risks, and the regulatory context, were identified along with twenty-one sub-themes such as noise and falling materials. Findings reveal a fragmented regulatory landscape, combining byelaws, national laws, and EU regulations, creating business uncertainty. Our study recommends the establishment of specific national guidelines for commercial drone use, addressing uncertainties and building public trust, especially in anticipation of the integration of ‘autonomous’ drones. This research contributes to the responsible computing domain by uncovering regulatory gaps and issues in UK drone law, particularly within the often-overlooked context of the construction sector. The insights provided aim to inform future responsible computing practices and policy development in the evolving landscape of commercial drone technology.

The behaviours of a swarm are not explicitly engineered. Instead, they are an emergent consequence of the interactions of individual agents with each other and their environment. This emergent functionality poses a challenge to safety assurance. The main contribution of this paper is a process for the safety assurance of emergent behaviour in autonomous robotic swarms called AERoS, following the guidance on the Assurance of Machine Learning for use in Autonomous Systems (AMLAS). We explore our proposed process using a case study centred on a robot swarm operating a public cloakroom.

Disabled people are often involved in robotics research as potential users of technologies which address specific needs. However, their more generalised lived expertise is not usually included when planning the overall design trajectory of robots for health and social care purposes. This risks losing valuable insight into the lived experience of disabled people, and impinges on their right to be involved in the shaping of their future care. This project draws upon the expertise of an interdisciplinary team to explore methodologies for involving people with disabilities in the early design of care robots in a way that enables incorporation of their broader values, experiences and expectations. We developed a comparative set of focus group workshops using Community Philosophy, LEGO® Serious Play® and Design Thinking to explore how people with a range of different physical impairments used these techniques to envision a “useful robot”. The outputs were then workshopped with a group of roboticists and designers to explore how they interacted with the thematic map produced. Through this process, we aimed to understand how people living with disability think robots might improve their lives and consider new ways of bringing the fullness of lived experience into earlier stages of robot design. Secondary aims were to assess whether and how co-creative methodologies might produce actionable information for designers (or why not), and to deepen the exchange of social scientific and technical knowledge about feasible trajectories for robotics in health-social care. Our analysis indicated that using these methods in a sequential process of workshops with disabled people and incorporating engineers and other stakeholders at the Design Thinking stage could potentially produce technologically actionable results to inform follow-on proposals.

The role of Artificial Intelligence (AI) in clinical decision-making raises issues of trust. One issue concerns the conditions of trusting the AI which tends to be based on validation. However, little attention has been given to how validation is formed, how comparisons come to be accepted, and how AI algorithms are trusted in decision-making. Drawing on interviews with collaborative researchers developing three AI technologies for the early diagnosis of pulmonary hypertension (PH), we show how validation of the AI is jointly produced so that trust in the algorithm is built up through the negotiation of criteria and terms of comparison during interactions. These processes build up interpretability and interrogation, and co-constitute trust in the technology. As they do so, it becomes difficult to sustain a strict distinction between artificial and human/social intelligence.

Swarm robotics has begun to move from the laboratory to the real world. Potential applications include: logistics, environmental monitoring, search and rescue, and medicine. However, a key challenge for the deployment and use of these systems is the interrelated factor of ensuring technical, human and societal trust. To help address this complex issue, we first turn our attention to research on the technical properties of swarms, such as proficiency, scalability, robustness, and adaptability and their role in building trust. Second, we explore the area of research known as Human-Swarm Interaction, which studies how humans understand, monitor, control, and interact with swarms. Third, we focus on techniques used to specify, verify and validate swarms. Finally, we discuss the future of swarm robotics and conclude by suggesting areas of advancement which may help build human and society’s trust towards robot swarms.

The role of Artificial Intelligence (AI) in clinical decision-making raises issues of trust. One issue concerns the conditions of trusting the AI which tends to be based on validation. However, little attention has been given to how validation is formed, how comparisons come to be accepted, and how AI algorithms are trusted in decision-making. Drawing on interviews with collaborative researchers developing three AI technologies for the early diagnosis of pulmonary hypertension (PH), we show how validation of the AI is jointly produced so that trust in the algorithm is built up through the negotiation of criteria and terms of comparison during interactions. These processes build up interpretability and interrogation, and co-constitute trust in the technology. As they do so, it becomes difficult to sustain a strict distinction between artificial and human/social intelligence.