Conclusions and Recommendations, are particularly interesting in view of the controversies aroused by the Warnock Report. Many of the recommendations contained here are similar to Warnock's (for example, concerning the legitimacy of AID children, the need for a licensing authority to supervise the work ofAID and IVF centres, etc), but others are at odds with the corresponding Warnock recommendations. In general, the authors place higher value on the family as an institution than did the Warnock Committee and display a much livelier awareness of the possible social dangers of the new techniques. One weakness of the book is that since its authors are approaching these topics from the standpoint of social scientists, their recommendations for legislative action which surely must be based on properly ethical considerations, not merely sociological ones seem devoid of any satisfactory rational support. For example, they concede that experimentation on human embryos is an objectionable practice, since 'the material acting as the subject of the experimentation is a human being at the beginning of its individual development' (p 178); but the practical recommendation which they make concerning this practice is disappointingly feeble:
Introduction In all countries, some population groups experience barriers to accessing eye health services, contributing to health inequities. Outreach is a common strategy used to deliver healthcare services to populations experiencing inequities. This scoping review aims to summarise the nature and extent of the existing literature describing outreach as a service delivery model to improve access to eye health services, particularly among populations experiencing inequities.Methods and analysis An information specialist will search academic databases (Medline, Embase and Global Health) without language restrictions to find peer-reviewed articles describing outreach eye health services, published in any country between 1 January 2010 and the search date. Grey literature sources will also be searched. In Covidence, two reviewers will independently screen titles and abstracts and subsequently relevant full texts against the inclusion criteria. Data extraction will also be performed independently by two reviewers in Covidence. This scoping review will summarise the characteristics of the included outreach eye health services, including the type of eye health service delivered, personnel involved, mode of transport, source of funding and whether the service targeted any specific PROGRESS-Plus group (Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status, Social capital, Plus). We will present our findings quantitatively using diagrams, tables and graphs.Ethics and dissemination Ethics approval was not sought, as this scoping review will use only publicly available reports. The results of this review will be disseminated through publication in a peer-reviewed journal and will be presented at eye health conferences. It will offer valuable insights for eye health providers, health and social service providers and policymakers who are interested in improving access to eye health services for populations experiencing inequities. This scoping review will inform a project in New Zealand which aims to develop outreach eye health services to populations experiencing inequities, such as unhoused people and refugees.Registration This protocol was registered on the Open Science Framework on 11 November 2025 (https://osf.io/vyz32).
Tom Bisson, Henriette Voelker, Sanddhya Jayabalan
et al.
Large language model (LLM)-based AI agents are increasingly capable of complex clinical reasoning and may soon participate in medical decision-making with limited or no real-time human oversight. This shift raises fundamental questions about how the core principles of medical ethics (i.e., beneficence, nonmaleficence, autonomy, and justice) can be upheld when the clinical responsibility extends to autonomous systems. Here we propose an ethics-by-design framework for medical AI agents comprising six practical interventions: auditable ethical reasoning modules, explicit human override conditions, structured patient preference profiles, AI-specific ethics oversight tools, global benchmarking repositories for ethical scenarios, and regulatory sandboxes for real-world evaluation. Together, these mechanisms aim to operationalize ethical governance for emerging clinical AI agents. https://github.com/BissonTom/Ethical-Governance-of-Medical-AI-Agents
Acceleration ethics addresses the tension between innovation and safety in artificial intelligence. The acceleration argument is that risks raised by innovation should be answered with still more innovating. This paper summarizes the theoretical position, and then shows how acceleration ethics works in a real case. To begin, the paper summarizes acceleration ethics as composed of five elements: innovation solves innovation problems, innovation is intrinsically valuable, the unknown is encouraging, governance is decentralized, ethics is embedded. Subsequently, the paper illustrates the acceleration framework with a use-case, a generative artificial intelligence language tool developed by the Canadian telecommunications company Telus. While the purity of theoretical positions is blurred by real-world ambiguities, the Telus experience indicates that acceleration AI ethics is a way of maximizing social responsibility through innovation, as opposed to sacrificing social responsibility for innovation, or sacrificing innovation for social responsibility.
The integration of large language models into healthcare necessitates a rigorous evaluation of their ethical reasoning, an area current benchmarks often overlook. We introduce PrinciplismQA, a comprehensive benchmark with 3,648 questions designed to systematically assess LLMs' alignment with core medical ethics. Grounded in Principlism, our benchmark features a high-quality dataset. This includes multiple-choice questions curated from authoritative textbooks and open-ended questions sourced from authoritative medical ethics case study literature, all validated by medical experts. Our experiments reveal a significant gap between models' ethical knowledge and their practical application, especially in dynamically applying ethical principles to real-world scenarios. Most LLMs struggle with dilemmas concerning Beneficence, often over-emphasizing other principles. Frontier closed-source models, driven by strong general capabilities, currently lead the benchmark. Notably, medical domain fine-tuning can enhance models' overall ethical competence, but further progress requires better alignment with medical ethical knowledge. PrinciplismQA offers a scalable framework to diagnose these specific ethical weaknesses, paving the way for more balanced and responsible medical AI.
The pervasive integration of artificial intelligence (AI) across domains such as healthcare, governance, finance, and education has intensified scrutiny of its ethical implications, including algorithmic bias, privacy risks, accountability, and societal impact. While ethics has received growing attention in computer science (CS) education more broadly, the specific pedagogical treatment of {AI ethics} remains under-examined. This study addresses that gap through a large-scale analysis of 3,395 publicly accessible syllabi from CS and allied areas at leading Indian institutions. Among them, only 75 syllabi (2.21%) included any substantive AI ethics content. Three key findings emerged: (1) AI ethics is typically integrated as a minor module within broader technical courses rather than as a standalone course; (2) ethics coverage is often limited to just one or two instructional sessions; and (3) recurring topics include algorithmic fairness, privacy and data governance, transparency, and societal impact. While these themes reflect growing awareness, current curricular practices reveal limited depth and consistency. This work highlights both the progress and the gaps in preparing future technologists to engage meaningfully with the ethical dimensions of AI, and it offers suggestions to strengthen the integration of AI ethics within computing curricula.
The mathematisation of the socio-economic sphere, where mathematics actively constructs social reality, presents a challenge for studies on ethics in mathematics and its education. While existing scholarship on ethics in mathematics offers insights, it often remains philosophically driven and disconnected from other relevant disciplines. This paper addresses this gap by asking how debates on ethics in mathematics and its education can be connected with economic sociology, and what socio-economic tensions become visible through this connection. Drawing from concepts such as imagined futures, varieties of capitalism, and variegated capitalism, we synthesise a new perspective. This analysis reveals six interconnected tensions: a socio-economic valuation gap regarding ethics education; the multifaceted implementation of mathematics across different capitalist systems; its material opaqueness; a growing gap between economic power and social unaccountability; the enclosure of imagination limiting sustainable futures; and the erosion of multilateralism, which challenges critical pedagogy. The paper's contribution is a first step towards a structural socio-economic framework that links the limited literature on ethics in mathematics with these broader sociological perspectives.
Yasir Abdelgadir Mohamed, Abdul Hakim H. M. Mohamed, Akbar Khanan
et al.
This review examines the ethical, social, and technical challenges posed by AI-generated text tools, focusing on their rapid advancement and widespread adoption. An exhaustive literature search across many databases, strict inclusion/exclusion criteria, and a rigorous analysis procedure are all parts of our systematic review technique. This guarantees an impartial and complete study of the current status of AI-generated text tools. The study analyzes prominent language models, including GPT-3, GPT-4, LaMDA, PaLM, Claude, Jasper, and Llama 2, evaluating their capabilities in natural language processing and generation. The analysis reveals significant advancements, with GPT-3 demonstrating a 92% accuracy rate on standard natural language understanding benchmarks, outperforming LaMDA (88%) and PaLM (85%). To illustrate real-world implications, the review presents a case study of ChatGPT’s application in healthcare, where it achieved 80% consistency with expert opinions in assessing acute ulcerative colitis. This case highlights both the potential benefits and ethical concerns of AI in critical domains. Quantitative bias analysis shows that GPT-3 generated biased content in 15% of test cases involving sensitive topics, a higher rate than LaMDA (12%) and PaLM (10%). We provide an in-depth analysis of fairness and bias issues, particularly in image generation tasks depicting professional roles. Our research synthesizes insights from technical advancements, ethical considerations, and real-world applications across healthcare, education, and creative sectors. We address critical privacy concerns and data protection challenges, noting struggles in AI-generated text detection and investigating AI’s potential in enabling cyberattacks. We underscore the need for comprehensive governance systems and multidisciplinary cooperation. To provide a cohesive analysis of the ethical considerations surrounding AI-generated text tools, we employ a multifaceted ethical framework drawing on established theories. Utilitarianism, which seeks to maximize happiness for everyone; deontology, which places an emphasis on right and wrong; and Virtue Ethics, which analyzes the moral nature of deeds and actors, are all included in this framework. In this article, we use this approach to investigate AI ethics from a variety of angles, including privacy, prejudice, and social implications, as well as concerns of justice and fairness. Moreover, the study critically examines existing and proposed legal frameworks addressing AI ethics, identifying regulatory gaps and proposing adaptive policy recommendations to address the unique challenges posed by AI-generated text tools. Our review contributes a critical analysis of AI-generated text tools, their impacts, and the need for responsible innovation. The study provides precise guidelines for the ethical development and implementation of AI, highlighting the need to strike a balance between technical progress and ethical concerns to guarantee that AI technologies have a beneficial effect on society while protecting human values. The emergence of generative artificial intelligence (AI) signifies a substantial revolution in our methods of interacting with language and information.