The recent advancements in artificial intelligence (AI), and data science more broadly, have led to a proliferation of new methods and tools, such as machine learning (ML), that are used in all kinds of scientific research, from biomedical research through to environmental and education research. Research ethics review bodies are increasingly required to review AI research protocols that cover these different fields of enquiry. Questions have been raised regarding the appropriateness of existing ethics governance principles, practices, and processes to deal with the ethical challenges that AI and data science are introducing to research. Universities and research institutions across the world are trying to understand how to translate and practically implement broad AI ethical principles into research ethics governance guidelines and processes. In this article, we report on an expert stakeholders’ workshop organised at the University of Oxford as part of the process of reviewing its ethics governance for AI research. We describe the workshop and present the reflections and recommendations that emerged from it. The aim of the article is to share the approach taken by the University of Oxford CUREC in reviewing its ethics governance processes, and the insights gained with the broader research community, as a way of contributing to this scarce body of literature, facilitating further dialogue, promoting debate and collaboration on this important issue.

The development of novel technologies does not take place in a moral, economic, or geopolitical vacuum. Therefore, questions of funding, use-cases, and contexts of use will directly influence both moral and practical judgements around the ways in which these technologies are, or should be, developed and deployed. This article examines the growth of Quantum Technologies (QTs) in light of these challenges, and the ways in which they are already raising concerns with respect to questions of ‘dual-use’. The article argues that ‘dual-use’ as a category is inadequate for normative judgement as it conflates moral evaluation with a descriptive classification, and therefore misses important contextual and procedural elements. The article proposes making Responsible Innovation (RI) frameworks integral to the development of QTs in order to enable context-sensitive ethical assessment and decision-making guidance. Finally, the article proposes a way forward that engages with the principle-based concerns of those critical of military uses of QTs, while respecting the challenges posed by current geopolitical realities.

Responsible Research and Innovation (RRI) promotes inclusive, anticipatory, and reflexive research practices that respond to societal needs. While widely applied in technological fields, its application in youth mental health remains limited. This study aimed to explore how RRI principles are understood and enacted within a large interdisciplinary programme on digital youth mental health in the United Kingdom, focusing on the perspectives of both researchers and young people. An online survey was conducted with 21 researchers and 5 young people (mean age = 21 years, standard deviation = 2.74) involved in the programme. The survey included open-ended questions exploring knowledge, attitudes, and practices related to RRI and youth mental health. Responses were analysed using Reflexive Thematic Analysis to identify patterns of meaning across the dataset and to generate themes. Six themes were developed, reflecting participants’ knowledge, attitudes, and practices. Both researchers and young people conceptualised youth mental health as multifaceted, shaped by personal, social, and cultural factors, and existing along a continuum from flourishing to struggling. Young people highlighted digital harms and economic precarity, while researchers emphasised biopsychosocial determinants, offering complementary perspectives. Involving young people was seen as essential for challenging adult assumptions, improving clarity and relevance of tools, and strengthening ethical integrity. Barriers included communication gaps, entrenched hierarchies, inconsistent involvement, and the resource-intensive nature of participation. Key facilitators included mutual respect, care, flexibility, and procedural structures such as youth co-chairs (i.e., a young person co-leading the project/grant with the principal investigator/s) and regular collaborative meetings. Together, these elements demonstrated how RRI values can be embedded to foster meaningful and equitable youth–researcher partnerships. This study shows that applying RRI in youth mental health research enhances co-production by integrating diverse perspectives, addressing ethical concerns, and strengthening the quality and social relevance of research. To fully realise this potential, RRI must be embedded as an ongoing practice supported by intentional infrastructures, such as youth leadership roles, communication training, and opportunities for intergenerational dialogue. Crucially, funders must recognise and resource the relational, iterative, and time-intensive nature of responsible youth involvement. Embedding RRI in this field provides a valuable framework for moving beyond tokenistic consultation towards inclusive, future-oriented, and ethically grounded research. This study looked at how to do research with young people, not just about them, in the area of youth mental health. We used the idea of “Responsible Research and Innovation” (RRI), which means planning ahead, listening carefully, and acting responsibly so that research is useful, fair, and safe for the people it affects. We asked researchers and young people who were working together to share their views and experiences. Both groups said youth mental health is complex and sits on a spectrum from doing well to struggling, shaped by personal factors (like sleep, mood, and coping) and social factors (like family, friends, money, and online life). Young people said their involvement helped challenge adult researchers’ assumptions and made tools and studies clearer, more relevant, and easier to use. They also pointed out practical needs for good partnerships: mutual respect, plain language, quick feedback, and real influence on decisions. Risks were flagged too – discussions can be emotionally tough, and privacy must be protected. Working together is rewarding but demanding. Jargon, time pressures, and power imbalances can get in the way. To fix this, the study suggests: involve young people from the very start; create roles like a Youth Co-chair; provide training to support good communication across ages; and bring young people and policymakers together for regular roundtables. We also call on funders to back this work properly, with time, flexibility, and resources. Done well, RRI helps produce safer, fairer, and more effective research for young people’s mental health.

Quantum technologies (QT) are advancing rapidly, promising advancements across a wide spectrum of applications but also raising significant ethical, societal, and geopolitical impacts, including dual-use capabilities, varying levels of access, and impending quantum divide(s). To address these, the Responsible Quantum Technologies (ResQT) community was established to share knowledge, perspectives, and best practices across various disciplines. Its mission is to ensure QT developments align with ethical principles, promote equity, and mitigate unintended consequences. Initial progress has been made, as scholars and policymakers increasingly recognize principles of responsible QT. However, more widespread dissemination is needed, and as QT matures, so must responsible QT. This paper provides a structured, situated synthesis of the ResQT community's current work and identifies future directions from within this community perspective. Drawing on historical lessons from artificial intelligence and nanotechnology, actions targeting the quantum divide(s) are addressed, including the implementation of responsible research and innovation, fostering wider stakeholder engagement, and sustainable development. These actions aim to build trust and engagement, facilitating the participatory and responsible development of QT. The ResQT community advocates that responsible QT should be an integral part of quantum development rather than an afterthought so that quantum technologies evolve toward a future that is technologically advanced and beneficial for all.

The way in which responsible innovation (RI) is interpreted and implemented depends to a significant degree on how the researchers, scholars and practitioners who work on RI see their own work. There have been several contributions to the RI discourse suggesting distinctive roles that RI researchers may adopt. In this article we present the findings of an empirical study of roles of RI researchers in research projects based on an online survey collecting both quantitative and qualitative data on the roles that RI researchers assume. Its findings confirm that RI researchers assume some of the roles described in the literature. However, the data shows that some of the roles from the literature were not adopted and there were several additional roles not part of the literature. We propose a model that will allow for more detailed research on the factors that influence role choices of researchers..

Abstract Dr Carolyn Ten Holter, postdoctoral research associate in Oxford's Responsible Technology Institute and member of BCS' Quantum Computing Specialist Group, gives an introduction to quantum technology and its real-world possibilities.

This paper reports on a qualitative research study that explored the practical and emotional experiences of young people aged 13-17 using algorithmically-mediated online platforms. It demonstrates an RI-based methodology for responsible two-way dialogue with the public, through listening to young people's needs and responding to their concerns. Participants discussed in detail how online algorithms work, enabling the young people to reflect, question, and develop their own critiques on issues related to the use of internet technologies. The paper closes with action areas from the young people for a fairer, usefully transparent and more responsible online environment. These actions include a desire to be informed about what data (both personal and situational) is collected and how, and who uses it and why, and policy recommendations for meaningful algorithmic transparency and accountability. Finally, participants claimed that whilst transparency is an important first principle, they also need more control over how platforms use the information they collect from users, including more regulation to ensure transparency is both meaningful and sustained.

This article reports results from a public survey designed to evaluate public attitudes and perceptions towards data recorders in Autonomous Vehicles (AVs). Our study indicated that road users are willing to make compromises about their privacy in and around AVs, as long as the data recorded is used to improve vehicle safety. Our study also indicated that more vulnerable road-users such as pedestrians, cyclists and horse-riders are willing to be recorded by on-board devices, however this willingness is linked to the data from these devices being accessible to determine liability and the cause of an accident or near-miss. However, the type of data recording currently mandated by international legal frameworks does not accord with these public expectations. While the results of our survey highlights a gap between the international legal obligations of manufacturers and the expectations of the public, it is also relevant to inform policy makers at the national level on the public's view about the importance of data recorders in the development of trustworthy autonomous vehicles. The failure of AVs to meet societal expectations on transparent data recording frameworks, and the use of that data to improve safety, may impact the uptake and acceptance of AVs.

Societal trust in research and innovation is predicated on factors such as governance, safety, and responsible development. These are often thought of as regulatory matters, but regulation may be ill-adapted for many novel technologies. Anticipatory governance, potentially in the form of responsible innovation (RI), can help to provide this adaptivity and granularity. However, RI remains new to many fields, and can be difficult to apply. This paper analyses the literature to identify challenges for RI and lessons from other domain frameworks, synthesising this with empirical evidence to develop a Framework. The Beehive Framework is a straightforward to use, translatable scaffold, with accompanying guidance for mapping and recording RI within projects of various scales and types. Its iterative process model approach to RI contains elements of project management methodology, and captures information gathered during the processes of RI. It records these processes for further iteration, and comparison between projects.

As artificial intelligence (AI) technology advances, ensuring the robustness and safety of AI-driven systems has become paramount. However, varying perceptions of robustness among AI developers create misaligned evaluation metrics, complicating the assessment and certification of safety-critical and complex AI systems such as autonomous driving (AD) agents. To address this challenge, we introduce Simulation-Based Robustness Assessment Framework (S-RAF) for autonomous driving. S-RAF leverages the CARLA Driving simulator to rigorously assess AD agents across diverse conditions, including faulty sensors, environmental changes, and complex traffic situations. By quantifying robustness and its relationship with other safety-critical factors, such as carbon emissions, S-RAF aids developers and stakeholders in building safe and responsible driving agents, and streamlining safety certification processes. Furthermore, S-RAF offers significant advantages, such as reduced testing costs, and the ability to explore edge cases that may be unsafe to test in the real world. The code for this framework is available here: https://github.com/cognitive-robots/rai-leaderboard

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Many sources propose that Autonomous Vehicles (AVs) could offer societal benefits in the future. Although such claims frequently relate to safety, AVs will undoubtedly also create new types of incidents and accidents. Data related to failure or accidents will therefore be a fundamental requirement for ensuring safety, accountability and public trust. Regulations and standards are being developed for equipping AVs with data recorders, also known as Black Boxes. These devices can log different types of parameters related to the vehicle status and collect large volumes of data relating to the vehicle and the surrounding environment. Although data retrieval is vital to understanding the causes of an accident and contributing to ongoing safety developments, there can be ethical risks as well as legal, social and political implications related to collection, storage, processing, access and use of data. In this work, we took a responsible innovation approach to these questions, seeking to establish the current practice with regard to technology, people, and institutions involved with AV data recorders, evaluate the usefulness of present regulations for defining safety-critical scenarios, and identify gaps that could have significant consequences further down the line. We close with recommendations for future work and practice.

Novel technologies such as quantum computing present new opportunities to support societal needs, but societal engagement is vital to secure public trust. Quantum computing technologies are at a pivotal point in their journey from foundational research to deployment, creating a moment for society to investigate, reflect, and consult on their implications. Responsible Innovation (RI) is one method for considering impacts, engaging with societal needs, reflecting on any concerns, and influencing the trajectory of the innovation in response. This paper draws on the empirical work of the RI team embedded in the Networked Quantum Information Technologies Hub. The team investigated researchers' perceptions of RI and their understanding of societal impacts of quantum technologies, and sought to gauge the challenges of embedding RI across a multi-disciplinary, large-scale enterprise such as the UK quantum programme. The work demonstrated some of the difficulties involved in embedding RI approaches, and in creating a dialogue between innovators and societies. Finally, the authors offer recommendations to policymakers, researchers, and industrial organisations, for better practice in responsible quantum computing, and to ensure that societal considerations are discussed alongside commercial motivations. Applying RI to quantum computing at this pivotal point has implications for RI in other emerging technologies.

Innovations and efficiencies in digital technology have lately been depicted as paramount in the green transition to enable the reduction of greenhouse gas emissions, both in the information and communication technology (ICT) sector and the wider economy. This, however, fails to adequately account for rebound effects that can offset emission savings and, in the worst case, increase emissions. In this perspective, we draw on a transdisciplinary workshop with 19 experts from carbon accounting, digital sustainability research, ethics, sociology, public policy, and sustainable business to expose the challenges of addressing rebound effects in digital innovation processes and associated policy. We utilize a responsible innovation approach to uncover potential ways forward for incorporating rebound effects in these domains, concluding that addressing ICT-related rebound effects ultimately requires a shift from an ICT efficiency-centered perspective to a "systems thinking" model, which aims to understand efficiency as one solution among others that requires constraints on emissions for ICT environmental savings to be realized.

Purpose The purpose of the study detailed here was to engage with Directors of Centres for Doctoral Training (CDTs) during the first year of their new Centres to form a snapshot view of the nature and type of training that was being incorporated and how this might affect the wider institution – in this case the university. Using an organisational learning lens, this paper empirically examines the work-in-progress of the responsible innovation (RI) training in CDTs to assess how new RI understandings are being created, retained and transferred within the CDTs, questioning whether this process represents a programme of “institutionalisation”. Design/methodology/approach During the past decade, RI has become increasingly embedded within the EU and UK research context, appearing with greater frequency in funding calls and policy spaces. As part of this embedding, in its 2018 funding call for CDTs, the Engineering and Physical Sciences Research Council (EPSRC) required RI training to be included in the programme for all doctoral students. Findings The paper concludes that, at present, institutionalisation is highly variegated, with the greater organisational change required to truly embed RI mindsets. Originality/value The paper provides original, empirical research evidence of RI institutionalisation in UK CDTs, and, using a “learning organisation” lens, examines areas of value to both RI and learning organisation theory.

Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human’s cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies.

This paper draws on three case studies to examine some of the challenges and tensions involved in the use of Autonomous Decision-Making Systems (ADMS). In particular, the paper highlights: (i) challenges around the shifting “locale” of the decision, and the associated consequences for stakeholders; (ii) potential implications for stakeholders from regulation such as the General Data Protection Regulation (GDPR); (iii) the different values that stakeholder groups bring to the “decision” question; (iv) how complex pre-existing webs of stakeholders and decision-making authorities may be disrupted or disempowered by the use of an automated system and the lack of evaluation of possible consequences; (v) how ADMS for non-technical users can lead to circumvention of the boundaries of intended system use. We illustrate these challenges through case studies in three domains: adult social care, aviation, and vehicle driver monitoring systems. The paper closes with recommendations for both practice and policy in the deployment of ADMS.

Autonomous Vehicles (AVs) collect a vast amount of data during their operation (MBs/sec). What data is recorded, who has access to it, and how it is analysed and used can have major technical, ethical, social, and legal implications. By embedding Responsible Innovation (RI) methods within the AV lifecycle, negative consequences resulting from inadequate data logging can be foreseen and prevented. An RI approach demands that questions of societal benefit, anticipatory governance, and stakeholder inclusion, are placed at the forefront of research considerations. Considered as foundational principles, these concepts create a contextual mindset for research that will by definition have an RI underpinning as well as application. Such an RI mindset both inspired and governed the genesis and operation of a research project on autonomous vehicles. The impact this had on research outlines and workplans, and the challenges encountered along the way are detailed, with conclusions and recommendations for RI in practice.