Engineering ethics education often relies on case-study methods focused on disaster prevention and professional codes. Recent work in virtue ethics and professional identity has begun to develop a possible alternative, but the philosophical foundations of engineering’s ethical character remain underdeveloped. This paper argues that engineering constitutes a morally significant professional identity: a life-shaping practice whose internal goods are oriented toward the development of central human capabilities. Drawing on Appiah’s (2005, 2018) identity framework, MacIntyre’s (2007, 1999) theory of practices and his account of compartmentalization, the capability approach as formalized by Robeyns (2017), and Wolfendale’s (2007) regulative-ideal account of professional integrity, I analyze engineering identity through institutional analyses of France’s École Polytechnique and the United States’ Accreditation Board for Engineering and Technology (ABET). I argue that engineering’s normative regulative ideal is the development of human capabilities, and that this orientation provides the philosophical grounding for an identity-based approach to engineering ethics education that connects ethical development to the cultivation of engineering character. The account developed here deepens Davis’s (1991) identity-inflected reading of engineering professionalism and translates across the Franco-American tradition to non-Western frameworks (e.g., Lan et al., 2021) with which it converges.
Large language models have become central infrastructures of contemporary digital economies while raising persistent ethical concerns regarding linguistic inequality, opacity, data governance, and the concentration of technological power. Much of the current debate on AI ethics focuses on normative principles such as fairness, transparency, and accountability. While these principles remain essential, they often do not sufficiently explain why ethically problematic outcomes persist under competitive market conditions. This paper addresses that gap by applying Karl Homann’s institutional economic ethics to the governance of large language models. From this perspective, ethical deficits in AI development are not merely the result of individual failures of responsibility, but are also shaped by institutional incentive structures that reward speed, scale, proprietary control, and strategic secrecy. The paper analyses three central ethical challenges in LLM development: linguistic and cultural asymmetries, transparency and accountability deficits, and contested practices of data governance and intellectual property. It then argues that the familiar opposition between open and closed AI systems is conceptually and institutionally inadequate. In response, the paper develops the concept of partially open AI governance, understood as a differentiated arrangement of access, disclosure, and oversight across distinct layers of AI systems. Such an approach offers a more realistic way of aligning innovation incentives with ethical and public objectives in the governance of large language models.
Locator devices, also known as electronic tracking, monitoring, or surveillance devices, are increasingly being adopted in various care settings to help manage dementia-related wandering and bolster the safety and autonomy of persons with dementia. Their use in dementia-care has raised ethical concerns. While the primary focus of this concern has centered on the use of locator devices, there is a steadily growing call to better integrate ethical reflection into the development phase of these technologies. Many ethical tensions are present during the development of a locator device, and stakeholder inclusion, inclusive of persons with dementia, is a key method to navigating them. However, due to current limitations in dementia inclusive development, stakeholder inclusion alone is insufficient to fully engage with the ethical dimensions of locator device development. In this article, we aim to support more effective stakeholder inclusion by identifying underrepresented elements of the current normative debate and translating them into ethical considerations that can be used throughout the development process. We do this by applying the Leuven Ethical Question Framework for the evaluation of health technology innovations. Using the Framework as a tool for reflection, we identify six ethical considerations present at the individual-relational, organizational, societal, and global socio-historical contexts. These considerations can help integrate ethical reflection into the development process by providing developers with an initial scaffold for dialogue, both with stakeholders and within their own teams, and also serve as starting points for future research.
Technologies increasingly shape animals’ lives, yet animal interests are still largely marginal in mainstream technology ethics and in frameworks of Responsible Research and Innovation (RRI). This paper argues that this neglect constitutes a serious moral blind spot that sits in tension with these fields’ own ethical aspirations. Debates about the ethical acceptability and societal desirability of technologies often take human interests as the normative baseline. This anthropocentrism runs deep in both RRI and technology ethics, yet it is ethically indefensible for two mutually reinforcing reasons. First, technological innovations have profound direct and indirect impacts on animals, which will only intensify with the rise of new system technologies such as artificial intelligence. Second, as sentient beings, animals possess moral status and are worthy of moral consideration. Together, these grounds a responsibility to take animal interests seriously in our technological activities. To give this responsibility a practical foothold, I mobilise the concept of stakeholdership — a cornerstone of both RRI and technology ethics — arguing that reinterpreting it to include animals can facilitate their inclusion in ethical deliberation and decision-making about technology. The paper sketches potential pathways for recognising animals as stakeholders in ethical discussions on technology, and engages with some key challenges such an extension is likely to face. I conclude that only by broadening the moral scope beyond human concerns can RRI and technology ethics uphold their normative integrity, attend to a wider range of technological harms and benefits, and genuinely pursue ethically acceptable and socially desirable innovation.
Recommender systems are widely used to help users navigate information overload in digital environments. While they are often portrayed as tools that enhance autonomy by optimizing choice and personalizing content, this article argues that many current recommender systems designs in fact undermine user autonomy. Drawing on a conception of autonomy in which the formation and configuration of preferences, particularly higher-level desires in the Frankfurtian sense, play a prominent role, we examine how dominant recommendation techniques shape users’ desires and, to a greater extent, their identities. We first analyze what we call conjectural recommenders, systems that infer preferences from behavioral data such as clicks or viewing histories. These systems conflate observed choices with genuine preferences, reinforcing first-order desires, narrowing the diversity of recommendations, and trapping users in homogeneous digital environments that impede identity development and degrade deliberative capacities. We then assess interrogative recommenders, which incorporate explicit user feedback as a form of positive friction in the user interface. Although these recommenders prompt users to articulate evaluations, we argue that they remain insufficient for capturing second-order desires and raise new design challenges concerning paternalism and usability. In response, we propose the Socratic conversational recommender system, a model that combines conversational recommendation with a second layer of Socratic questioning aimed at eliciting users’ metapreferences. Rather than prescribing substantive values, the system guides reflection through formal criteria such as coherence, empirical awareness, and cognitive pluralism. The objective is not for the system to discover users’ true preferences but to foster their reflective engagement with what they want to want. We conclude that embedding deliberative dialogue within recommender design offers a promising pathway for aligning algorithmic recommendation with the preservation and development of human autonomy.
Artificial intelligence (AI) recruitment tools are becoming increasingly integrated into current hiring systems, where they support functions such as resume screening, pre-employment assessments, candidate engagement, and automated interviewing. While these technologies are often promoted as mechanisms for improving efficiency, objectivity, and accessibility, growing concerns have emerged regarding their implications for individuals with disabilities. Existing scholarship on AI recruitment tools and disability remains fragmented, with much of the literature emphasizing either opportunities or discriminatory risks without systematically or holistically examining both dimensions. To address this gap, this study conducted a systematic literature review examining the opportunities and risks individuals with disabilities encounter in AI-mediated hiring processes. Guided by PRISMA-informed procedures, we analyzed 21 studies published between 2020 and 2026 across multiple major databases related to AI, recruitment technologies, disability, accessibility, and hiring systems. The findings suggest that AI recruitment tools may create opportunities for more flexible and accessible hiring through adaptive technologies, alternative communication modalities, and reduced social pressure during interviews. However, the review also identifies substantial ethical risks arising across both the design and development stage and the use and engagement stage of AI recruitment tools. This review argues that AI recruitment tools should be understood not as neutral technologies, but as sociotechnical systems that embed institutional assumptions about professionalism, communication, and employability. Developing more equitable AI-mediated hiring systems therefore requires participatory, disability-centered, and culturally responsive approaches to AI design and governance.
The integration of micro- and macroethical perspectives remains a challenge in engineering ethics education (EEE). Fiction-based EEE has been advocated as a pedagogical approach that can help students in this regard. However, how students actually use fiction to engage with micro- and macroethics remains underexplored. Therefore, this case study aimed to (1) identify how students’ engagement with fictions connected to micro and macro perspectives on engineering and technology ethics, (2) examine how students realized pedagogical potentials of fiction-based pedagogy in this regard, and (3) explore how the outcomes related to the pedagogical framing and activities of the course. The selected case was an elective, fiction-based EEE course, during which the written course work of students, teacher and student interviews, course materials, and data from workshop observations were collected for qualitative analysis. Findings show that several expected pedagogical potentials of fiction-based pedagogy were realized as students reflected on what guides human behavior and on the societal impact of technology, both within and beyond the fictions. However, students’ reflections rarely connected to their future professional role. These findings could be related to characteristics of the employed pedagogy and fictions, but also to students’ prior knowledge and expectations regarding professional engineering practice. We conclude that students may need targeted support towards connecting fictions with self and profession, and that fiction-based EEE may benefit from recognizing and responding to students’ pre-existing expectations on social responsibility and ethical agency in engineering.
The rise of virtual worlds, collectively known as the Metaverse, compels a re-examination of longstanding debates concerning personal freedom, moral responsibility, and legal accountability. These immersive digital environments differ from prior communication technologies not merely in degree but in the phenomenological quality they produce: a pervasive sense of presence that blurs the boundary between the virtual and the physical. This article examines the ethical and legal challenges that arise when individuals choose to spend a substantial portion of their lives within such digital spaces. We introduce the concept of “Meta-Autonomy” as a theoretical framework for understanding personal agency in algorithmically mediated environments. We argue that existing accounts of autonomy — Kantian, procedural, and enactivist — each capture important dimensions of this challenge but face specific difficulties when confronted simultaneously with four structural properties of Metaverse environments: algorithmic mediation of choice architecture, radical fluidity of identity, networked and distributed causal agency, and the moral embeddedness of virtual experience. Meta-Autonomy is proposed as a framework that holds these four dimensions together and derives from their interaction both a set of diagnostic criteria for identifying autonomy deficits and a set of normative implications for regulation. We develop the thought experiment of “Plato’s Digital Cave” to illustrate the first-order/second-order autonomy distinction, and apply Meta-Autonomy’s four components systematically to specific legal questions — mens rea, legal personhood, distributed liability, virtual harm, and the regulation of Metaverse platforms under the GDPR, EU AI Act, and Digital Services Act. The article concludes by identifying concrete directions for future philosophical and legal research.
Dominant approaches to bias in artificial intelligence (AI) are structured by what I identify as the isolationist problem: the tendency to treat bias as a discrete, technically addressable flaw within the AI development pipeline, rather than as a relational phenomenon embedded in social, institutional, and political arrangements. This problem is sustained by two mutually reinforcing orientations: technocentrism, which reframes ethical challenges as engineering problems amenable to computational resolution, and the bias-centric conception of fairness, which reduces fairness to statistical mitigation and obscures its contested, context-dependent character. Together, these orientations produce ontological, epistemic, and practical forms of narrowing ethical imagination and channel intervention into technically tractable but socially limited responses. Against this, I propose sociotechnical sensitivity as both an analytical orientation and a normative commitment: a sustained attentiveness to the ways in which AI systems are constitutively embedded in social relations, institutional arrangements, cultural norms, and power structures. The paper’s central argumentative shift is to change the narrative from bias mitigation to bias management—treating bias not as a defect to be corrected but as an ongoing condition to be governed. These arguments are developed through an extended analysis of the well-known case of the COMPAS algorithm, a recidivism risk-assessment prediction tool, illustrating three practical axes of bias management: contextualisation, institutionalisation, and iteration. The gap between sociotechnically sensitive AI ethics and its realisation is ultimately a matter of governance design and political will, not merely a problem of missing methods or tools.
In this article, we investigate influences of social positionality, designer bias and educational exposure on algorithmic design in healthcare. Against the backdrop of literature on designer bias that points to how it contributes to disproportionate, discriminatory and unethical impacts for racialized and gendered bodies, this study tests this argument with an experiential case study involving Aldebaran’s NAO robot and mechatronics and robotics engineering students at a university in S. E. Ontario, Canada. To conduct the study, we adopted open-ended and semi-structured interviews. The study sought to address questions such as: what other social agents – besides engineers – are/should be involved in design? To what extent do engineers collaborate with other knowledge holders? From students’ point of view, how does engineering education prepare them to tackle ethical and social problems in design? Because of the small sample size, the intention is not to extrapolate beyond the data in this study but for the data to serve exploratory purposes in pursuit of further research.
Considerable effort has been invested during the last decade to develop ethical frameworks to guide the use of artificial intelligence (AI) technologies. Most of these frameworks take the form of an axiomatic list, that is, a list of principles or values taken as self-evident truths. This paper argues these lists face three challenges: divergence within the core principles, structural incoherence, and ethics washing. Three proposals are then put forward to address these challenges and ensure the effective implementation of AI ethics. First, an ethics framework must be parsimonious and, second, a framework building on multiple principles or values must also provide a balancing structure to deal with the different implications of these principles. The third proposal is to envision AI ethics as a process more than a static list of axiomatic principles or values.
Biopiracy represents not merely the exploitation of biological and genetic resources, but a systematic form of epistemic colonialism. This article examines traditional knowledge governance through the lens of epistemic justice and decolonization, integrating Fricker’s theory of epistemic injustice, Kuokkanen’s concept of relational sovereignty, and Santos’s epistemology of the South to construct a three-stage analytical model encompassing “biopiracy, epistemicide, and knowledge sovereignty reconstruction.” Through comparative analysis of institutional innovations in India, Brazil, and South Africa, the study reveals structural exclusion mechanisms inherent in Western property paradigms toward indigenous knowledge while demonstrating fundamental flaws in benefit-sharing mechanisms under the Convention on Biological Diversity framework. The article argues that sustainable knowledge governance must be grounded in epistemic pluralism, achieved through restoring indigenous communities’ epistemic autonomy, recognizing the normative value of relational sovereignty, and establishing polycentric knowledge ecosystems to facilitate a paradigmatic shift from colonial knowledge orders to post-Western knowledge systems.
We evaluate an intervention designed to give lab-based research teams an opportunity to intentionally discuss project-related data management practices within their labs, examining how such communication might inform perceptions of the relationship between formal data management plans and lab members’ day-to-day data management practices. In an earlier study, we developed a lab-based intervention that encouraged a deliberative approach to discussions among lab members regarding the practices of data management and authorship, and an exploration of the ethical dimensions of those practices. This present study builds on this prior work, both as partial replication and extension. Here we show the significant effects of the intervention across several dimensions, but importantly and specific to this project, this deliberative communication approach enhances the likelihood that lab members share an understanding of and a commitment to its data management practices, in part because they have been actively involved in shaping those practices. Fostering shared understanding and commitment is crucial for maintaining rigor and responsibility across all aspects of a lab’s work, and is essential for cultivating legitimate, defensible, and ethical approaches to producing scientific knowledge.
Concerns about the integrity of scientific research and the erosion of public trust in science led to policy recommendations to improve the responsible conduct of research (RCR). One recommendation was to increase scientific integrity through training, and numerous funding agencies mandated training in RCR for graduate students and postdoctoral fellows. Many institutions implemented training on consensus recommended topics. While many reports describe RCR training and demonstrate the effectiveness at the end of training, they do not report on its usefulness to the trainees in the real world. This study was initiated to address this gap and evaluate graduate student perceptions of the utility of coursework in RCR during their subsequent dissertation research. The study consisted of two cross-sectional survey studies conducted at a mid-sized university in the United States. The first captured the responses of first-year doctoral students enrolled in a semester-long course in RCR (n = 83 respondents). The second captured the responses of past participants in the course (n = 74 respondents). The results demonstrate that students enrolled in the course gain knowledge and self-efficacy from the course. Past participants in the course identified lessons learned from the course that helped them navigate or avoid ethical challenges in their research. The results demonstrate that students appreciate the value of training in research integrity and that they are applying concepts from their coursework very early in their careers.
In the ethical literature on robot design, considerable attention has been devoted to the risks that human-like robots pose to humans. In particular, the resemblance of robots to humans has been associated with concerns about social disruption, deception, and potential harm, leading some authors to argue for restrictions or bans in certain contexts. However, while the ethical significance of human-like form has been widely recognized for humans, significantly less attention has been paid to the implications of animal-like robots from the perspective of nonhuman animals and human–animal relations. This paper addresses that gap by examining how the design and deployment of animal-like robots may affect animals both directly and indirectly. We argue that the use of animal form in robotics is not ethically neutral, as it can shape human perceptions of animals, influence patterns of interaction, and potentially contribute to harms such as mislearning, objectification, and alienation. By shifting the focus from human-centered concerns to the impact on animals, we identify a set of ethically relevant risks associated with animal-like robots and analyze their broader implications for human–animal relations. On this basis, we propose preliminary guidelines aimed at supporting the responsible design and use of such technologies.
Engineering ethics education often emphasizes teaching reasoning skills while overlooking other influential dimensions on one’s ethical behavior – e.g., whether students see ethical responsibility as part of who they are as professionals. One of the challenges in integrating such overlooked dimensions in engineering ethics education is the limited resources in assessing student outcomes. To address this challenge, adapting a widely used moral identity scale to the engineering context, this paper introduces an initial development and validation effort of the Engineering Professional Moral Identity (EPMI) instrument, designed to assess the extent to which ethical responsibility is integrated into one’s identity as an engineer. Exploratory and confirmatory analyses were performed with survey data from 515 practicing engineers. Results supported a two-factor structure, internalization and symbolization, with an eight-item model. We argue that EPMI complements reasoning-focused measures by capturing the identity dimension of ethical development, which enables longitudinal tracking of students’ identity development and targeted instruction. Findings from this study provide initial validity evidence for EPMI and position the tool as a practical tool for assessing and cultivating identity-centered ethics education in engineering education.
Ethics is an essential aspect of professional development in engineering and required for accreditation in engineering programs. Educators need to understand how engineers experience ethics in their engineering practice to align ethics education with work experiences. We addressed the research question, “What are the qualitatively different ways engineers experience ethical engineering practice in the health products industry?” We used phenomenography to collect and analyze 43 interviews with practicing engineers working in orthopedics, medical devices, and pharmaceuticals. This methodology assumes (1) there are qualitatively different ways of experiencing a phenomenon (ethical engineering practice) and (2) ways of experiencing are structurally related. This research identified six categories of ways of experiencing ethical engineering practice: Doing Right, Ensuring Integrity in Processes, Upholding Professional Responsibility, Understanding and Reconciling Perspectives, Negotiating and Using Judgment, and Stewarding Culture. The structural relationships among the categories were comprised of two dimensions of variation: Understanding the System and Understanding Role and Responsibility. The findings provide a more comprehensive understanding of the varied ways to experience ethical engineering in industry practice and thus have potential to inform ethics education in engineering programs and in workforce training.
Questionable Research Practices (QRPs) are a highly diverse set of behaviours that eludes clear definition and demarcation. This study collects and organises definitions of QRPs given in surveys, and ranks them by frequency of reported engagement. We systematically retrieved surveys that asked researchers about their engagement with QRPs, and organised these survey definitions in two non-arbitrary classifications based, respectively, on the area of research affected by the QRP (e.g. authorship, data, analysis, etc…), and on the nature of the alteration of information entailed by the QRP (respectively, whether the QRP consisted in an omission, addition, or modification of information). Starting from this principled classification, we then created a list of non-overlapping QRP types that have been most commonly studied in surveys. We found that QRPs are more commonly reported when they pertain to the interpretation, analysis or citations, and when they involved the omission, rather than the addition or modification of information. Classified by type, the most commonly reported QRPs included “select analysis”, “select citation”, and “select covariates”; whilst the least commonly reported included “deny authorship”, and “FFP” (i.e. explicit fabrication, falsification, plagiarism, which are not QRPs but outright misconduct). Our QRP taxonomy and empirical results may find useful applications in research, training and policy-making.
Artificial intelligence (AI) is increasingly promoted as a tool to enhance clinical decision-making and thus improve the quality of healthcare. While much of the emerging scholarship on AI and healthcare in Africa has focused broadly on opportunities and systemic challenges, what remains underexplored is the specific application of AI to clinical decision-making. This paper contributes to addressing this gap by offering a conceptual and critical analysis of AI-based clinical decision support systems (AI-CDSS) in African contexts. Drawing on philosophical accounts of medical reasoning and relational moral frameworks such as Ubuntu, the paper draws on the moral ecology of care and shows that algorithmic systems can reconfigure epistemic authority, redistribute responsibility, and risk marginalising context-sensitive and relational dimensions of care. The paper further argues that AI systems are better understood as socio-technical mirrors that reflect and amplify existing human values, institutional arrangements, and power asymmetries. Moving beyond the algorithm, it proposes a shift toward relational and context-sensitive AI governance, including the development of relational impact assessments, the redistribution of responsibility across the AI lifecycle, and the co-production of knowledge with local stakeholders. While focusing on African clinical contexts, the analysis offers broader insights for global debates on AI ethics and clinical decision-making.
Starting in 2006, the ethics code of the US National Society of Professional Engineers (NSPE) states that engineers have professional obligations to encourage sustainable development. The organization continues to emphasize that sustainability is important for ensuring a community’s public health, safety, and welfare. Emphasizing sustainability requires considering impacts to both this generation and future generations. Investigations into the latter need more attention since there are many more studies focusing on the former. In this paper, we show how an ethic of care, particularly as it has been developed by Joan Tronto, can offer support for the engineering profession to practice an intergenerational ethic. At first glance, Tronto’s framing of care ethics might appear to be an unlikely candidate to meet obligations for future generations as this model involves “responsiveness” on the part of the caregiver to feedback offered by the one receiving care. In this paper, we demonstrate an alternative picture, where feedback is provided by proxies for future generations.
