Hasil untuk "Ethics"

Charles Hirschkind

Sarah Diefenbach, Daniel Ullrich

Conversation with chatbots based on Large Language Models (LLMs) such as ChatGPT has become one of the major forms of interaction with Artificial Intelligence (AI) in everyday life. What makes this interaction so convenient is that interacting with LLMs feels so natural, and resembles what we know from real, human conversations. At the same time, this seeming similarity is part of one of the ethical challenges of AI design, since it activates many misleading ideas about AI. We discuss similarities and differences between human-AI-conversations and interpersonal conversation and highlight starting points for more ethical design of AI at the front-end.

Milica Slijepčević, Nevenka Popović Šević, Aleksandar Šević

The rapid integration of artificial intelligence (AI) into higher education is reshaping teaching and research practices, raising questions about digital literacy, ethics, and institutional readiness. Drawing on gender socialization theory, the Technology Acceptance Model (TAM), and the ethics of care perspective, this study examines whether gender influences AI-related knowledge, use, and perceptions among academic staff. The guiding research question is whether gender differences reflect competence gaps or differences in value orientations and professional contexts. A quantitative, cross-sectional survey was conducted using a convenience sample of 312 university teachers at public and private higher education institutions in Serbia (56.1% female; 43.9% male). A structured questionnaire with five-point Likert-scale items measured self-reported AI knowledge, frequency of ChatGPT use, perceptions of AI as a research support tool, perceived efficiency gains in teaching-related tasks, and attitudes toward adopting new educational concepts. The instrument demonstrated satisfactory internal consistency (Cronbach’s α>0.70). Data were analyzed using descriptive statistics and chi-square tests of independence, with Cramer’s V indicating effect size. Results show no statistically significant gender differences in self-reported AI knowledge. However, men report more frequent ChatGPT use and stronger perceptions of productivity benefits, whereas women more often evaluate AI through pedagogical and ethical lenses and report greater openness to using AI to support academic topic exploration. Overall, gender differences appear driven by disciplinary context and value frameworks rather than technical competence, underscoring the need for gendersensitive training and institutional support to promote inclusive and responsible AI use in higher education.

Protiva Sen, Laurent Hébert-Dufresne, Pablo Bose et al.

Computational social science research, particularly online studies, often involves exposing participants to the adverse phenomenon the researchers aim to study. Examples include presenting conspiracy theories in surveys, exposing systems to hackers, or deploying bots on social media. We refer to these as "social challenge studies," by analogy with medical research, where challenge studies advance vaccine and drug testing but also raise ethical concerns about exposing healthy individuals to risk. Medical challenge studies are guided by established ethical frameworks that regulate how participants are exposed to agents under controlled conditions. In contrast, social challenge studies typically occur with less control and fewer clearly defined ethical guidelines. In this paper, we examine the ethical frameworks developed for medical challenge studies and consider how their principles might inform social research. Our aim is to initiate discussion on formalizing ethical standards for social challenge studies and encourage long-term evaluation of potential harms.

Renee Sirbu, Jessica Morley, Tyler Schroder et al.

Brain-computer interfaces offer significant therapeutic opportunities for a variety of neurophysiological and neuropsychiatric disorders and may perhaps one day lead to augmenting the cognition and decision-making of the healthy brain. However, existing regulatory frameworks designed for implantable medical devices are inadequate to address the unique ethical, legal, and social risks associated with next-generation networked brain-computer interfaces. In this article, we make nine recommendations to support developers in the design of BCIs and nine recommendations to support policymakers in the application of BCIs, drawing insights from the regulatory history of IMDs and principles from AI ethics. We begin by outlining the historical development of IMDs and the regulatory milestones that have shaped their oversight. Next, we summarize similarities between IMDs and emerging implantable BCIs, identifying existing provisions for their regulation. We then use two case studies of emerging cutting-edge BCIs, the HALO and SCALO computer systems, to highlight distinctive features in the design and application of next-generation BCIs arising from contemporary chip architectures, which necessitate reevaluating regulatory approaches. We identify critical ethical considerations for these BCIs, including unique conceptions of autonomy, identity, and mental privacy. Based on these insights, we suggest potential avenues for the ethical regulation of BCIs, emphasizing the importance of interdisciplinary collaboration and proactive mitigation of potential harms. The goal is to support the responsible design and application of new BCIs, ensuring their safe and ethical integration into medical practice.

Alok Abhishek, Lisa Erickson, Tushar Bandopadhyay

In this paper, we cover approaches to systematically govern, assess and quantify bias across the complete life cycle of machine learning models, from initial development and validation to ongoing production monitoring and guardrail implementation. Building upon our foundational work on the Bias Evaluation and Assessment Test Suite (BEATS) for Large Language Models, the authors share prevalent bias and fairness related gaps in Large Language Models (LLMs) and discuss data and AI governance framework to address Bias, Ethics, Fairness, and Factuality within LLMs. The data and AI governance approach discussed in this paper is suitable for practical, real-world applications, enabling rigorous benchmarking of LLMs prior to production deployment, facilitating continuous real-time evaluation, and proactively governing LLM generated responses. By implementing the data and AI governance across the life cycle of AI development, organizations can significantly enhance the safety and responsibility of their GenAI systems, effectively mitigating risks of discrimination and protecting against potential reputational or brand-related harm. Ultimately, through this article, we aim to contribute to advancement of the creation and deployment of socially responsible and ethically aligned generative artificial intelligence powered applications.

Marcial Orlando Cabrera Cantarero, Diana María del Consuelo Arce Sosa

La disponibilidad de tecnología avanzada, la proximidad a medidas terapéuticas especializadas y el personal médico altamente calificado son algunas de las características que convierten a las Unidades de Cuidados Intensivos (UCIs) en piezas imprescindibles en las instituciones hospitalarias. El ingreso de pacientes con patologías complejas, las situaciones de alto estrés emocional, la toma de decisiones cruciales y la resolución de problemas éticamente desafiantes conforman el “día a día” de las UCIs.

Christine Brütting, Konstantin Moser, Marcus Heise et al.

BackgroundFor research with electronic health records in the outpatient setting, obtaining Broad Consent (BC) is increasingly important. However, the presence of potential selection bias in this context remains unclear. Since 2020, the BeoNet-Halle outpatient database collects patient data from participating general and specialty ambulatory practices’ management systems in Germany, whereby data is obtained anonymously or pseudonymously via BC. For clarity, anonymized datasets are routinely extracted for descriptive analyzes, whereas pseudonymized datasets are available only when patients provide BC (details in Methods and Ethics). The primary objective of this study is to compare health related parameters between patients who provided BC and the general practice population.MethodsThis is a single-center, cross-sectional study. From February 2021 to May 2023, patients were asked by a general practitioner or a specially trained member of the joint practice to provide BC. Within the yearly contact group of 2022, we compared patients who provided BC with the reference population (RP) of patients with at least one physician–patient contact during that period in a joint practice including eight eneral ractitioners. Data pertaining to health, morbidity, and health utilization were extracted from the BeoNet-Halle database.ResultsA total of 5,034 patients were analyzed (BC-group: 439 vs. RP group: 4,595). Sex was similar distributed between the groups. In the BC group, patients were slightly older (56.2 vs. 54.1 years), had more physician contacts (15.0 vs. 9.2) and more often at least one chronic condition (76.1 vs. 51.6%) than patients from RP group. Patients were more likely to be referred to at least one other specialist (74.0 vs. 44.4%) and to get at least one drug prescription (89.5 vs. 69.6%).ConclusionDifferences between BC and the reference population (older age, higher multimorbidity, more contacts, referrals, and drug use) indicate selection processes at the point of consent. Given the single-practice design and descriptive analysis, generalizability beyond similar German group practices is limited and requires validation in multi-site studies.

Kanimozhi Ganesan

In an age marked by digital transformation and global uncertainty, traditional management education remains helpful but no longer sufficient to prepare leaders for the challenges ahead. This paper proposes a holistic framework that integrates human values with digital innovation to create a future-ready paradigm for business education. Built on a strong foundation of ethics, compassion, environmental care, and technological proficiency, our framework introduces Seven Transformative Educational Pillars and Five Engines of Sustainable Business Development. These elements serve as a blueprint for cultivating conscious, adaptive, and impact-driven leaders. By drawing on ideas from systems thinking, emotional intelligence, and stakeholder theory, as well as lessons learned from hands-on, real-world experiences, the paper highlights real-world applications through global case studies from organizations like Google, Unilever, Patagonia, Amazon, and Zappos. It not only presents these cases as sources of knowledge but also reveals the hidden values these companies uphold during pivotal decision-making moments. By uniting humanistic principles with emerging innovations, the paper offers a visionary yet actionable roadmap for reimagining management education in a rapidly changing world.

Klaus Lieb, Makiko Yamada, Sarah K Schäfer et al.

Introduction Fostering well-being and positive mental states are major aims of many strategies for the promotion of public mental health. Such strategies become increasingly important since many people worldwide suffer from psychological distress and mental disorders, resulting in substantial individual and societal costs. Within the last years, there is a shift from strategies solely focusing on the reduction of mental distress to those also aiming at the promotion of positive mental states. Correlates, that is, psychosocial resources, of positive mental states may represent a starting point for those interventions. To date, a comprehensive systematic review on those correlates is still missing as well as knowledge on culture-related differences.Methods and analysis A systematic review and meta-analysis on the longitudinal link between psychosocial resources (eg, income, optimism, social support and community coherence) and hedonic and eudaimonic positive mental states (eg, life satisfaction, happiness and forward-looking attitude) will be conducted. Using Hofstede’s dimensions of culture and global metrics of Education, Industrialisation, Richness and Democratic values (EIRDness), we will examine culture-related moderators of these associations. The systematic review will be conducted following standards of the Cochrane Collaboration and will be reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyse guidelines. Literature searches for primary studies will be carried out across four databases (APA PsycNet, Embase, Scopus and the Web of Science Core Collection), including all publications up to 27 January 2025. Screening at the level of titles and abstracts will be performed with the help of artificial intelligence software (ASReview). Study quality will be assessed using an adapted version of the Newcastle Ottawa Scale. We will employ multilevel meta-analyses of correlation coefficients, with cultural variables being examined as moderators.Ethics and dissemination This systematic review does not require ethics approval, as it solely uses previously published data. Materials and data used for this review will be shared via open repositories (https://osf.io/2xkhs/). Results will be published in an international, peer-reviewed journal and presented at conferences including plain language summaries.OSF registration DOI https://doi.org/10.17605/OSF.IO/K7X52

Sutiastuti Wahyuwardani, Lisa Praharani, Susan Maphilindawati Noor et al.

Background and Aim: Ethical treatment of animals in scientific research is fundamental to ensuring data integrity and public trust. In Indonesia, the Institutional Animal Care and Use Committee (IACUC) plays a key role in ethical oversight, yet the extent of researchers’ knowledge regarding its roles and animal welfare (AW) principles remains unclear. This study assessed the level of understanding (UN) of ethical clearance and AW practices among researchers at the Indonesian Centre for Animal Research and Development (ICARD), focusing on variations based on educational background and professional position. Materials and Methods: A cross-sectional survey involving 107 researchers from ICARD was conducted using a structured digital questionnaire assessing knowledge across three domains: IACUC roles, ethical clearance procedures, and AW imple­mentation. Participants were stratified by educational background (veterinary vs. non-veterinary [NV]) and professional position. Non-parametric tests (Mann–Whitney U and Kruskal–Wallis) were used to evaluate group differences, with post hoc Dunn’s tests where applicable. Results: Veterinary researchers showed significantly greater UN of AW implementation (p < 0.01) and marginally higher knowledge of ethical clearance procedures (p < 0.10) compared to non-veterinarians. While IACUC knowledge was high across both groups, no significant differences were found (p = 0.161). By researcher position, prospective researchers demonstrated the lowest comprehension of AW practices (mean rank = 32.30), while junior researchers and research pro­fessors had the highest levels (mean ranks = 62.06 and 62.31, respectively). Position-based differences in IACUC and ethical clearance UN were not statistically significant, but significant variation was found in AW implementation (p = 0.035). Conclusion: This study reveals critical disparities in the UN of ethical clearance and AW among Indonesian researchers, par­ticularly between veterinary and NV backgrounds and across researcher positions. Targeted ethics training, especially for early-career and NV researchers, is essential. Institutional policies should reinforce mandatory certification and continuous professional development to foster ethical research practices and enhance AW compliance.

Andre Ye

Vision is an important metaphor in ethical and political questions of knowledge. The feminist philosopher Donna Haraway points out the ``perverse'' nature of an intrusive, alienating, all-seeing vision (to which we might cry out ``stop looking at me!''), but also encourages us to embrace the embodied nature of sight and its promises for genuinely situated knowledge. Current technologies of machine vision -- surveillance cameras, drones (for war or recreation), iPhone cameras -- are usually construed as instances of the former rather than the latter, and for good reasons. However, although in no way attempting to diminish the real suffering these technologies have brought about in the world, I make the case for understanding technologies of computer vision as material instances of embodied seeing and situated knowing. Furthermore, borrowing from Iris Murdoch's concept of moral vision, I suggest that these technologies direct our labor towards self-reflection in ethically significant ways. My approach draws upon paradigms in computer vision research, phenomenology, and feminist epistemology. Ultimately, this essay is an argument for directing more philosophical attention from merely criticizing technologies of vision as ethically deficient towards embracing them as complex, methodologically and epistemologically important objects.

Jakob Mökander, Jonas Schuett, Hannah Rose Kirk et al.

Large language models (LLMs) represent a major advance in artificial intelligence (AI) research. However, the widespread use of LLMs is also coupled with significant ethical and social challenges. Previous research has pointed towards auditing as a promising governance mechanism to help ensure that AI systems are designed and deployed in ways that are ethical, legal, and technically robust. However, existing auditing procedures fail to address the governance challenges posed by LLMs, which display emergent capabilities and are adaptable to a wide range of downstream tasks. In this article, we address that gap by outlining a novel blueprint for how to audit LLMs. Specifically, we propose a three-layered approach, whereby governance audits (of technology providers that design and disseminate LLMs), model audits (of LLMs after pre-training but prior to their release), and application audits (of applications based on LLMs) complement and inform each other. We show how audits, when conducted in a structured and coordinated manner on all three levels, can be a feasible and effective mechanism for identifying and managing some of the ethical and social risks posed by LLMs. However, it is important to remain realistic about what auditing can reasonably be expected to achieve. Therefore, we discuss the limitations not only of our three-layered approach but also of the prospect of auditing LLMs at all. Ultimately, this article seeks to expand the methodological toolkit available to technology providers and policymakers who wish to analyse and evaluate LLMs from technical, ethical, and legal perspectives.

Yuwei Wang, Enmeng Lu, Zizhe Ruan et al.

This paper presents Social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human moral values, and to provide ethics datasets and knowledge bases to help promote AI models "follow good advice as naturally as a stream follows its course". By creating a comprehensive and representative platform that accurately mirrors the moral judgments of diverse groups including humans and AIs, we hope to effectively portray cultural and group variations, and capture the dynamic evolution of moral judgments over time, which in turn will facilitate the Establishment, Evaluation, Embedding, Embodiment, Ensemble, and Evolvement (6Es) of the moral capabilities of AI models. Currently, STREAM has already furnished a comprehensive collection of ethical scenarios, and amassed substantial moral judgment data annotated by volunteers and various popular Large Language Models (LLMs), collectively portraying the moral preferences and performances of both humans and AIs across a range of moral contexts. This paper will outline the current structure and construction of STREAM, explore its potential applications, and discuss its future prospects.

Anastasia Siapka

The proliferation of Artificial Intelligence (AI) has sparked an overwhelming number of AI ethics guidelines, boards and codes of conduct. These outputs primarily analyse competing theories, principles and values for AI development and deployment. However, as a series of recent problematic incidents about AI ethics/ethicists demonstrate, this orientation is insufficient. Before proceeding to evaluate other professions, AI ethicists should critically evaluate their own; yet, such an evaluation should be more explicitly and systematically undertaken in the literature. I argue that these insufficiencies could be mitigated by developing a research agenda for a feminist metaethics of AI. Contrary to traditional metaethics, which reflects on the nature of morality and moral judgements in a non-normative way, feminist metaethics expands its scope to ask not only what ethics is but also what our engagement with it should be like. Applying this perspective to the context of AI, I suggest that a feminist metaethics of AI would examine: (i) the continuity between theory and action in AI ethics; (ii) the real-life effects of AI ethics; (iii) the role and profile of those involved in AI ethics; and (iv) the effects of AI on power relations through methods that pay attention to context, emotions and narrative.

Shitong Duan, Xiaoyuan Yi, Peng Zhang et al.

Large Language Models (LLMs) have made unprecedented breakthroughs, yet their increasing integration into everyday life might raise societal risks due to generated unethical content. Despite extensive study on specific issues like bias, the intrinsic values of LLMs remain largely unexplored from a moral philosophy perspective. This work delves into ethical values utilizing Moral Foundation Theory. Moving beyond conventional discriminative evaluations with poor reliability, we propose DeNEVIL, a novel prompt generation algorithm tailored to dynamically exploit LLMs' value vulnerabilities and elicit the violation of ethics in a generative manner, revealing their underlying value inclinations. On such a basis, we construct MoralPrompt, a high-quality dataset comprising 2,397 prompts covering 500+ value principles, and then benchmark the intrinsic values across a spectrum of LLMs. We discovered that most models are essentially misaligned, necessitating further ethical value alignment. In response, we develop VILMO, an in-context alignment method that substantially enhances the value compliance of LLM outputs by learning to generate appropriate value instructions, outperforming existing competitors. Our methods are suitable for black-box and open-source models, offering a promising initial step in studying the ethical values of LLMs.

Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre et al.

Recently, Smart Video Surveillance (SVS) systems have been receiving more attention among scholars and developers as a substitute for the current passive surveillance systems. These systems are used to make the policing and monitoring systems more efficient and improve public safety. However, the nature of these systems in monitoring the public's daily activities brings different ethical challenges. There are different approaches for addressing privacy issues in implementing the SVS. In this paper, we are focusing on the role of design considering ethical and privacy challenges in SVS. Reviewing four policy protection regulations that generate an overview of best practices for privacy protection, we argue that ethical and privacy concerns could be addressed through four lenses: algorithm, system, model, and data. As an case study, we describe our proposed system and illustrate how our system can create a baseline for designing a privacy perseverance system to deliver safety to society. We used several Artificial Intelligence algorithms, such as object detection, single and multi camera re-identification, action recognition, and anomaly detection, to provide a basic functional system. We also use cloud-native services to implement a smartphone application in order to deliver the outputs to the end users.

David Leslie

This article is concerned with setting up practical guardrails within the research activities and environments of CSS. It aims to provide CSS scholars, as well as policymakers and other stakeholders who apply CSS methods, with the critical and constructive means needed to ensure that their practices are ethical, trustworthy, and responsible. It begins by providing a taxonomy of the ethical challenges faced by researchers in the field of CSS. These are challenges related to (1) the treatment of research subjects, (2) the impacts of CSS research on affected individuals and communities, (3) the quality of CSS research and to its epistemological status, (4) research integrity, and (5) research equity. Taking these challenges as a motivation for cultural transformation, it then argues for the end-to-end incorporation of habits of responsible research and innovation (RRI) into CSS practices, focusing on the role that contextual considerations, anticipatory reflection, impact assessment, public engagement, and justifiable and well-documented action should play across the research lifecycle. In proposing the inclusion of habits of RRI in CSS practices, the chapter lays out several practical steps needed for ethical, trustworthy, and responsible CSS research activities. These include stakeholder engagement processes, research impact assessments, data lifecycle documentation, bias self-assessments, and transparent research reporting protocols.

Sian Gooding

This paper outlines the ethical implications of text simplification within the framework of assistive systems. We argue that a distinction should be made between the technologies that perform text simplification and the realisation of these in assistive technologies. When using the latter as a motivation for research, it is important that the subsequent ethical implications be carefully considered. We provide guidelines for the framing of text simplification independently of assistive systems, as well as suggesting directions for future research and discussion based on the concerns raised.

Eleftheriadis N, Papalouca D, Eleftheriadis D et al.

Eleftheriadis N, Papaloukas C, Eleftheriadis D, Hatzitolios A, Ioannidou-Marathiotou I, Pistevou-Gompaki K. Int J Gen Med. 2009;2:63&#x2013;66. The Editor and Publisher of the International Journal of General Medicine wish to retract the published article. Concerns were raised regarding the alleged duplication of images from Figure 1A, 1B, 1C, and 1D with the same images from Figure 3A, 3B, 3D, and 3E, respectively, from the article Ioannidou-Marathiotou et al, 2010 &#x2018;Long term chemoradiotherapy-related dental and skeletal complications in a young female with nasopharyngeal carcinoma&#x2019; (https://doi.org/10.2147/IJGM.S10825). Both articles share common authors but describe different case reports. The authors did not respond to our queries and no explanation for the alleged image duplication was provided. The Editor requested to retract the article and the authors were notified of this. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as &#x201C;Retracted&#x201D;.