This paper aims to reflect on ethical considerations for the well-being of researchers involved in the development of virtual reality (VR) scenarios for parents who emotionally maltreat their children. Ethical considerations, particularly potential emotional strain, arose throughout the VR scenario development, including topic selection and content creation. However, despite the significance of these issues, existing literature addressing ethical challenges in researching sensitive topics from the researcher’s perspective has focused solely on data collection or analysis, overlooking the well-being of researchers involved in the development phase. To uphold high ethical standards, research should recognize and safeguard the well-being and safety of researchers throughout the study. In this methodological paper, the focus is on how an interdisciplinary team of researchers from technical and human sciences addressed the above concerns in biweekly reflective discussions. In these meetings, the team members shared their experiences and emotional reactions related to the development of VR scenarios simulating parents who were emotionally abusive toward their child. These reflective discussions created a secure environment for the researchers to process complex ethical issues and find emotional support, which is crucial for protecting their mental well-being from the outset of the project. The discussions contributed to strengthening the team’s overall well-being. In conclusion, this paper highlights the necessity of regular reflective meetings during the development phase to enhance the ethical quality of data gathering and reporting. In addition, engaging in team-based reflection improves researcher well-being and fosters ethical awareness. Reflective discussions have the potential to prevent vicarious traumatization by strengthening resilience when exposed to sensitive topics and addressing complex societal challenges.

This paper analyses temporality and the experience of increased speed in contemporary academia and its perceived influence on knowledge production. The study is grounded in theories of acceleration-related change in academia and examines the perceived effects of accelerated research processes on contemporary knowledge production, offering insights into the widespread use of questionable research practices (QRPs). Drawing on 36 focus group interviews with 172 researchers across seven European countries, the analysis reveals strikingly consistent perceptions of current reward and merit systems in academia, transcending differences in countries, disciplines, gender, and levels of seniority. These systems are widely perceived to reward speed and quantity at the expense of quality and integrity, thereby potentially undermining the robustness of the knowledge produced. The study thus shows a need for a stronger qualitative component in the assessment system, which aligns with the aims of the ‘research assessment reform movement’ (cf. DORA, CoARA). However, this paper argues that the research assessment reform movement needs to consider temporality in its efforts to reform research assessments. A stronger focus on ‘process time’ and ‘temporal autonomy’ is recommended to ensure that researchers and research units have adequate time to carry out all research tasks while maintaining integrity.

Horizon scanning is intended to identify opportunities and threats associated with technology, regulatory, and social change. Here, we report the results of a new horizon scan based on inputs of an international group of 33 participants, focusing on future issues arising from the military use of artificial intelligence (AI) for augmenting human performance. The final list of 12 issues includes topics spanning from the political (educating and training individuals to accept and work with AI), to the regulatory (issues of consent to human-AI teaming and hybridization), to security (the hackability of neural devices that connect to AI), to philosophical (the nature and phenomenology of brain-to-brain interfaces). The early identification of such issues is relevant to researchers, policymakers, military practitioners, and the wider public.

This study evaluates the formation of ethical responsibility among master's students enrolled in the "Applied Physics and Nanomaterials" program. Recognizing the pivotal role of ethics in the evolving field of nanoscience, the research implements a three-phase methodology that integrates an Initial Ethical Responsibility Assessment, a facilitator-led Group Discussion, and a Retest Ethical Responsibility Assessment. The assessments are based on realistic scenarios reflecting ethical dilemmas that students may encounter in professional practice. This dual approach - combining quantitative assessment with qualitative analysis - provides a comprehensive understanding of students' ethical reasoning and decision-making. Results indicate varying levels of ethical responsibility, underscoring the need for more integrated ethics education in nanoscience curricula. The study contributes to ongoing discussions on the importance of ethics in scientific training, especially in high-impact fields such as nanotechnology. It offers educators a structured framework for embedding ethics into technical education, ensuring that future nanomaterials specialists are not only proficient scientists but also ethically responsible professionals. The article concludes with recommendations for enhancing ethics education in nanoscience through interactive, scenario-based, and discussion-centered learning methods.

Emotion is an increasingly important concept in ethics, education, and engineering. It is also important for the intersection of these three domains: engineering ethics education. Despite extensive research being conducted independently in each field, there has yet to be a synthesis across the fields which would identify key themes, concepts or theories in use, and which would identify conceptual spaces for development. That is the goal of this paper. Our extensive systematic review identified 30 publications relevant to engineering, technology, or computer science education that were substantively focused on emotions and ethics. We coded these abductively and analyzed them thematically to identify underlying theoretical frameworks and concepts. Most publications included theorizations of emotion, ethics, or moral reasoning, and the ways they are related. Three – primarily psychological – theoretical frameworks were widely used: (A) empathy and pro-social action, (B) moral emotions, and (C) emotional intelligence/ emotional regulation. Possible intersections and relationships between these three frameworks were, however, largely unexplored in the included publications. We conclude that (1) researchers can break down conceptual silos by engaging with the relationships between different theories of emotion and ethics, (2) exploring academic emotions – emotions in the process of learning engineering ethics – presents considerable opportunities for further development, and (3) there is a need to broaden the theoretical base to supplement the current individualistic focus with more social theories of emotion and ethics.

The rise of automation, such as artificial intelligence, has significantly reduced human agency in artifact-involving actions, thereby giving rise to a moral dilemma known as the responsibility gap. In this dilemma, moral undesirable consequences arise from technology, yet no one is held accountable because individuals lack control over their actions and their outcomes, as well as the ability to predict these outcomes, rendering them morally blameless. This paper proposes a framework based on externalist agency and distributed responsibility, aiming to address this problem by assigning accountability to artifacts. Externalist agency holds that agency is a gradient concept rather than an all-or-nothing one. Between fully autonomous agency and non-agency, there are three intermediate gradients: distributed agency, constrained agency, and derivative agency. Distributed responsibility holds that responsibility should be allocated according to these gradients of agency. For both humans and artifacts: if an agent possesses derivative agency, forgiveness is warranted; if it possesses distributed agency, punishment is justifiable. This paper further suggests that punishment for artifacts can take the form of punishment for technological lineage—that is, modifying the design of technologies to guide their evolution toward safer and more ethical directions.

In recent years, the notion of ‘sustainable AI’ has emerged as a new topic within the wider debate on artificial intelligence (AI). Although sustainability is usually understood as having three dimensions – the environment, society, and the economy – the debate on sustainable AI, so far, is characterized by a narrow focus on the environmental sustainability of AI. In this article, it is argued that the debate on sustainable AI should not only be about AI’s environmental costs, but also incorporate social and economic concerns. More precisely, the article shows that AI’s environmental impact is often connected to important social and economic issues. Through an analysis of existing literature on the sustainability of AI, it is demonstrated that ethical and philosophical arguments about the importance of considering the environmental costs of AI apply just as well to the social and economic dimensions of AI development. The aim of this article is thus to present a broader understanding of sustainable AI; one that includes all three pillars of sustainability and acknowledges the interdependence between AI’s environmental, social and economic costs.

We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identify, map, and relate biases across the AI development process. This approach addresses the limitations of what we call the "isolationist approach to AI bias," a trend in AI literature where biases are seen as separate occurrences linked to specific stages in an AI pipeline. Dealing with these multiple biases can trigger a sense of excessive overload in managing each potential bias individually or promote the adoption of an uncritical approach to understanding the influence of biases in developers’ decision-making. The BNA fosters dialogue and a critical stance among developers, guided by external experts, using graphical representations to depict biased connections. To test the BNA, we conducted a pilot case study on the "waiting list” project, involving a small AI developer team creating a healthcare waiting list NPL model in Chile. The analysis showed promising findings: (i) the BNA aids in visualizing interconnected biases and their impacts, facilitating ethical reflection in a more accessible way; (ii) it promotes transparency in decision-making throughout AI development; and (iii) more focus is necessary on professional biases and material limitations as sources of bias in AI development.