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Synthetic biology, which aims to design and build organisms that serve human needs, has potential applications that range from producing biofuels to programming human behavior. The emergence of this new form of biotechnology, however, raises a variety of ethical questions -- first and foremost, whether synthetic biology is intrinsically troubling in moral terms. Is it an egregious example of scientists \"playing God\"?Synthetic Biology and Moralitytakes on this threshold ethical question, as well as others that follow, offering a range of philosophical and political perspectives on the power of synthetic biology.The contributors consider the basic question of the ethics of making new organisms, with essays that lay out the conceptual terrain and offer opposing views of the intrinsic moral concerns; discuss the possibility that synthetic organisms are inherently valuable; and address whether, and how, moral objections to synthetic biology could be relevant to policy making and political discourse. Variations of these questions have been raised before, in debates over other biotechnologies, but, as this book shows, they take on novel and illuminating form when considered in the context of synthetic biology.ContributorsJohn Basl, Mark A. Bedau, Joachim Boldt, John H. Evans, Bruce Jennings, Gregory E. Kaebnick, Ben Larson, Andrew Lustig, Jon Mandle, Thomas H. Murray, Christopher J. Preston, Ronald Sandler

We stand on the brink of unprecedented growth in our ability to understand and change the human genome. New reproductive technologies now enable parents to select some genetic traits for their children, and soon it will be possible to begin to shape ourselves as a species. Despite the loud cries of alarm that such a prospect inspires, Ronald Green argues that we will-and we should-undertake the direction of our own evolution. A leader in the bioethics community, Green offers a scientifically and ethically informed view of human genetic self-modification and the possibilities it opens up for a better future. Fears of a terribleBrave New Worldor a new eugenics movement are overblown, he maintains, and in the more likely future, genetic modifications may improve parents' ability to enhance children's lives and may even promote social justice. The author outlines the new capabilities of genomic science, addresses urgent questions of safety that genetic interventions pose, and explores questions of parenting and justice. He also examines the religious implications of gene modification. Babies by design are assuredly in the future, Green concludes, and by making responsible choices as we enter that future, we can incorporate gene technology in a new age of human adventure.

How does cooperation emerge among selfish individuals? When do people share resources, punish those they consider unfair, and engage in joint enterprises? These questions fascinate philosophers, biologists, and economists alike, for the \"invisible hand\" that should turn selfish efforts into public benefit is not always at work. The Calculus of Selfishness looks at social dilemmas where cooperative motivations are subverted and self-interest becomes self-defeating. Karl Sigmund, a pioneer in evolutionary game theory, uses simple and well-known game theory models to examine the foundations of collective action and the effects of reciprocity and reputation.

Artificial intelligence (AI) is rapidly transforming health care, offering substantial advancements in patient care, clinical workflows, and nursing education. This umbrella review aims to evaluate the integration of AI into nursing practice and education, with a focus on ethical and social implications, and to propose evidence-based recommendations to support the responsible and effective adoption of AI technologies in nursing. We included systematic reviews, scoping reviews, rapid reviews, narrative reviews, literature reviews, and meta-analyses focusing on AI integration in nursing, published up to October 2024. A new search was conducted in January 2025 to identify any potentially eligible reviews published thereafter. However, no new reviews were found. Eligibility was guided by the Sample, Phenomenon of Interest, Design, Evaluation, Research type framework; databases (PubMed or MEDLINE, CINAHL, Web of Science, Embase, and IEEE Xplore) were searched using comprehensive keywords. Two reviewers independently screened records and extracted data. Risk of bias was assessed with Risk of Bias in Systematic Reviews (ROBIS) and A Measurement Tool to Assess Systematic Reviews, version 2 (AMSTAR 2), which we adapted for systematic and nonsystematic review types. A thematic synthesis approach, conducted independently by 2 reviewers, identified recurring patterns across the included reviews. The search strategy yielded 18 eligible studies after screening 274 records. These studies encompassed diverse methodologies and focused on nursing professionals, students, educators, and researchers. First, ethical and social implications were consistently highlighted, with studies emphasizing concerns about data privacy, algorithmic bias, transparency, accountability, and the necessity for equitable access to AI technologies. Second, the transformation of nursing education emerged as a critical area, with an urgent need to update curricula by integrating AI-driven educational tools and fostering both technical competencies and ethical decision-making skills among nursing students and professionals. Third, strategies for integration were identified as essential for effective implementation, calling for scalable models, robust ethical frameworks, and interdisciplinary collaboration, while also addressing key barriers such as resistance to AI adoption, lack of standardized AI education, and disparities in technology access. AI holds substantial promises for revolutionizing nursing practice and education. However, realizing this potential necessitates a strategic approach that addresses ethical concerns, integrates AI literacy into nursing curricula, and ensures equitable access to AI technologies. Limitations of this review include the heterogeneity of included studies and potential publication bias. Our findings underscore the need for comprehensive ethical frameworks and regulatory guidelines tailored to nursing applications, updated nursing curricula to include AI literacy and ethical training, and investments in infrastructure to promote equitable AI access. Future research should focus on developing standardized implementation strategies and evaluating the long-term impacts of AI integration on nursing practice and patient outcomes.

Artificial intelligence is an essential part of our lives – for better or worse. It can be used to influence what we buy, who gets shortlisted for a job and even how we vote. Without AI, medical technology wouldn't have come so far, we'd still be getting lost on backroads in our GPS-free cars, and smartphones wouldn't be so, well, smart. But as we continue to build more intelligent and autonomous machines, what impact will this have on humanity and the planet? Professor Toby Walsh, a world-leading researcher in the field of artificial intelligence, explores the ethical considerations and unexpected consequences AI poses – Is Alexa racist? Can robots have rights? What happens if a self-driving car kills someone? What limitations should we put on the use of facial recognition? Machines Behaving Badly is a thought-provoking look at the increasing human reliance on robotics and the decisions that need to be made now to ensure the future of AI is as a force for good, not evil.

Background The integration of Generative artificial intelligence (GAI) into healthcare is rapidly evolving, necessitating ethical preparedness among nursing students. GAI technologies present ethical challenges related to patient privacy, algorithmic bias, and informed consent, underscoring the need for structured AI ethics education in nursing curricula. This study aims to examine the impact of an ethics education program on nursing students’ AI ethical awareness, moral sensitivity, attitudes, and intentions to use GAI in healthcare. Methods A quasi experimental, pretest‒posttest study was conducted with 115 nursing students. The participants were randomly assigned to an intervention group ( n  = 57), which received a structured AI ethics education program, or a control group ( n  = 58), which did not receive the intervention. The validated scales measured AI ethical awareness, moral sensitivity, attitudes, and the intention to use AI before and after the intervention. Results Compared with the control group, the intervention group demonstrated a significant increase in AI ethical awareness (M = 57.28, SD = 22.28) (M = 47.43, SD = 24.04; p  = .025, η² = 0.044). Moral sensitivity also showed a notable improvement in the intervention group (M = 74.33, SD = 29.93) compared with the control group (M = 60.26, SD = 22.52; p  = .005, η² = 0.067). Compared with the control group, positive attitudes toward AI significantly increased postintervention (M = 39.46, SD = 11.51) (M = 23.21, SD = 11.72; p  < .001, η² = 0.332), indicating a strong effect of ethics education. Furthermore, the intention to use AI technology improved significantly in the intervention group (M = 12.46, SD = 3.55) compared with the control group (M = 10.24, SD = 3.15; p  = .001, η² = 0.099). However, negative attitudes toward GAI did not significantly change postintervention. Conclusion This study highlights the effectiveness of structured AI ethics education in enhancing ethical competencies among nursing students. Integrating such programs into nursing curricula is essential to prepare future nurses for ethical decision-making in AI-driven healthcare. These findings support the development of standardized ethics training modules to guide responsible AI use in clinical practice and inform future curriculum design. Clinical trial number Not applicable.

The emergence of ethical concerns surrounding artificial intelligence (AI) has led to an explosion of high-level ethical principles being published by a wide range of public and private organizations. However, there is a need to consider how AI developers can be practically assisted to anticipate, identify and address ethical issues regarding AI technologies. This is particularly important in the development of AI intended for healthcare settings, where applications will often interact directly with patients in various states of vulnerability. In this paper, we propose that an ‘embedded ethics’ approach, in which ethicists and developers together address ethical issues via an iterative and continuous process from the outset of development, could be an effective means of integrating robust ethical considerations into the practical development of medical AI.

Background Artificial intelligence (AI) has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following question: What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective? Methods Eight electronic databases were searched for peer reviewed and grey literature published before April 2018 using the concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data charting form, and a descriptive and thematic analysis was performed. Results Upon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability and responsibility, and bias. Largely missing from the literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries (LMICs). Conclusions The ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere.

A major obstacle for the design of rigorous, reproducible studies in moral psychology is the lack of suitable stimulus sets. Here, we present the Socio-Moral Image Database (SMID), the largest standardized moral stimulus set assembled to date, containing 2,941 freely available photographic images, representing a wide range of morally (and affectively) positive, negative and neutral content. The SMID was validated with over 820,525 individual judgments from 2,716 participants, with normative ratings currently available for all images on affective valence and arousal, moral wrongness, and relevance to each of the five moral values posited by Moral Foundations Theory. We present a thorough analysis of the SMID regarding (1) inter-rater consensus, (2) rating precision, and (3) breadth and variability of moral content. Additionally, we provide recommendations for use aimed at efficient study design and reproducibility, and outline planned extensions to the database. We anticipate that the SMID will serve as a useful resource for psychological, neuroscientific and computational (e.g., natural language processing or computer vision) investigations of social, moral and affective processes. The SMID images, along with associated normative data and additional resources are available at https://osf.io/2rqad/.

The increasing integration of Artificial Intelligence (AI) in the medical domain signifies a transformative era in healthcare, with promises of improved diagnostics, treatment, and patient outcomes. However, this rapid technological progress brings a concomitant surge in ethical challenges permeating medical education. This paper explores the crucial role of medical educators in adapting to these changes, ensuring that ethical education remains a central and adaptable component of medical curricula. Medical educators must evolve alongside AI’s advancements, becoming stewards of ethical consciousness in an era where algorithms and data-driven decision-making play pivotal roles in patient care. The traditional paradigm of medical education, rooted in foundational ethical principles, must adapt to incorporate the complex ethical considerations introduced by AI. This pedagogical approach fosters dynamic engagement, cultivating a profound ethical awareness among students. It empowers them to critically assess the ethical implications of AI applications in healthcare, including issues related to data privacy, informed consent, algorithmic biases, and technology-mediated patient care. Moreover, the interdisciplinary nature of AI’s ethical challenges necessitates collaboration with fields such as computer science, data ethics, law, and social sciences to provide a holistic understanding of the ethical landscape.