Search Results (1,174)

Advances in machine learning, predictive analytics, and clinical prediction modeling have accelerated the development of algorithmic tools for estimating reproductive outcomes after cancer treatment. In female oncofertility counseling, these models may support individualized assessment of treatment-related amenorrhea, premature ovarian insufficiency, and fertility risk, thereby improving risk communication and timely fertility-preservation referral. However, their use raises ethical concerns beyond predictive accuracy. This narrative review examines algorithmic prognostication in female oncofertility counseling, focusing on predictive uncertainty, surrogate reproductive endpoints, missing data, heterogeneous datasets, limited external validation, algorithmic bias, reproductive inequity, and the influence of algorithmic authority on patient autonomy and shared decision-making. We argue that predictive algorithms should be understood as decision-support tools rather than determinants of reproductive futures. Responsible implementation requires transparency, explainability, fairness assessment, ongoing validation, and meaningful human oversight. Algorithmic risk estimates should be communicated as conditional and contextual probabilities within patient-centered counseling, ensuring that predictive tools support informed, transparent, and value-concordant fertility-preservation decisions for women facing cancer treatment. Full article

This study examines how Female-Associated Leadership Practices (FALPs) are associated with firm performance in Ecuadorian small and medium-sized enterprises (SMEs) through organizational governance and strategic orientation. Drawing on Upper Echelons Theory, social role theory, expectation states theory, and gender and leadership research, FALPs are conceptualized as perceived leadership practices frequently linked to participative decision-making, collaboration, ethical consideration, innovation stimulation, and long-term orientation. This conceptualization avoids treating leader gender, gender diversity, women’s representation in management, and leadership style as interchangeable constructs. Using a quantitative cross-sectional design, firm-level data from 300 SMEs were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that FALPs are not directly associated with firm performance. Instead, they are positively associated with governance quality and strategic orientation, both of which are, in turn, positively associated with firm performance. Strategic orientation represents the strongest indirect pathway, while governance is associated with performance both directly and indirectly through strategic orientation. These findings provide a process-based explanation for inconsistent evidence on gender-related leadership practices and firm outcomes. Practically, the study suggests that participative, ethical, collaborative, innovation-oriented, and long-term leadership practices may support SMEs by strengthening governance quality and strategic capabilities. Full article

Professional and elite sport represents a high-pressure healthcare environment in which standard principles of patient-centered care, confidentiality, informed consent, shared decision-making, and clinical independence are tested by organizational and performance-related pressures. This narrative review examines reliance management in athlete–physician relationships. Reliance is conceptually distinguished from trust: whereas trust is an interpersonal attitude that can be betrayed, reliance denotes the broader dependence of the athlete-patient on the physician, the care process, and the surrounding governance system—a dependence that may persist even where trust is absent. Reliance management is conceptualized as the deliberate, multi-level design and maintenance of communicative, ethical, and governance conditions under which such reliance is well-placed. A structured, non-systematic literature search of PubMed, Scopus, Web of Science, SPORTDiscus, and Google Scholar, supplemented by backward citation tracking, informed a thematic synthesis organized around three dimensions: clinical ethics, patient-centered care, and governance. The synthesis distinguishes empirical findings, professional consensus guidance, ethical and legal analysis, and illustrative cases, and identifies domains in which evidence remains weak. An integrative conceptual model is proposed that links governance antecedents at the dyadic, clinical, organizational, and system levels to mechanisms of well-placed reliance and to outcomes that require empirical evaluation. Proposed safeguards—independent medical oversight, documented return-to-play pathways, formal data-sharing rules, second-opinion access, and contractual protection of physician autonomy—are intended to protect patient-centered care and patient safety; their effectiveness has, however, rarely been evaluated and should be the object of future intervention research. Full article

This study examined elementary pre-service teachers’ artificial intelligence (AI) literacy in ChatGPT-4o-assisted science lesson design. Eight third-year pre-service teachers at a national university of education in Korea participated in a voluntary, non-credit, ten-week program focused on elementary Earth science lessons and used GPT-4o through the web-based ChatGPT interface. Data included pre-task written reflections, ChatGPT interaction logs, lesson plans and instructional artifacts, reflective journals, observation notes, and final reflective reports. A framework-guided thematic analysis examined AI knowledge, AI skills, AI ethics, and AI attitudes, while participant-level trajectories and lesson-design artifacts were analyzed across data sources. Initial AI literacy was heterogeneous rather than uniformly limited. Five of the eight participants articulated some pattern-based, probabilistic, or context-sensitive understanding of ChatGPT, seven of the eight anticipated using prompts that specified instructional conditions, all eight recognized a need to verify AI-generated content, and initial attitudes ranged from affirmative willingness to predominant skepticism. During the program, six of the eight participants showed increasingly contextualized or iterative prompting, and seven of the eight demonstrated artifact-linked verification, revision, supplementation, or alternative tool selection. Attitudinal trajectories were characterized by conditional acceptance rather than a uniform increase in trust. Across the eight participant-level trajectories, the analysis identified refinement, continuity, limited change, and one episode-specific awareness–practice inconsistency. These findings indicate that ChatGPT-assisted science lesson design can provide a practice-based context for examining AI literacy as situated professional judgment involving scientific, curricular, pedagogical, and ethical decision-making, without implying a common developmental progression. Full article

Soil sealing is a major driver of urban transformation, generating significant environmental impacts through the loss of soil functions and Ecosystem Services (ES). In this context, economic valuation of ES has emerged as an effective tool for communicating the importance of soil within decision-making processes largely influenced by market dynamics. Despite extensive research on ES, reviews focusing on how economic valuation can support local planning remain limited. As a consequence, this study presents a semi-systematic literature review addressing two research questions: (1) how ES assessment can be integrated into local planning tools and practices, and (2) which monetary metrics can ethically and effectively represent ES values within planning frameworks. Conducted in RStudio, the review combined automated screening of 2500 records with further manual selection and an analytical framework to examine ES definitions, valuation methods, spatial assessment units, and planning applications. The findings indicate that the rigor, methods, and level of detail of ES assessments should be adapted to planning objectives, available resources, territorial context, and stakeholders involved, supporting site-specific decisions. A cross-scale approach emerges as the most suitable for integrating ecological processes with administrative planning, while robust, flexible, and participatory governance across administrative levels is essential. Finally, despite the need for a shared methodological framework, standardized approaches such as the System of Environmental Economic Accounting–Ecosystem Accounting (SEEA EA) are still rarely adopted, particularly for local-scale and monetary ES assessments. Overall, the review provides a transparent and operational approach for integrating ES into spatial planning through interdisciplinary and participatory approaches and strengthening planning systems to prevent soil sealing and promote long-term community benefits. Full article

Artificial intelligence (AI) is increasingly being investigated to support decision-making and process improvement in copper mineral processing and extractive metallurgy; however, the available evidence remains fragmented across operational units, methods, sustainability dimensions, and geographical contexts. This systematic mapping review, complemented by qualitative cross-study synthesis, analysed publications from 2014 to 2025 retrieved from IEEE Xplore, Scopus, and Google Scholar. Of the 410 records identified before screening, 71 studies were classified according to four predefined research questions addressing copper-processing operations, AI domains, sustainability contributions, and geographical distribution. The findings show an uneven distribution of research across the processing flowsheet. Comminution and froth flotation received the greatest attention, whereas lixiviation, solvent extraction, electrowinning, and electrorefining were less represented. Machine learning was the dominant AI domain, particularly supervised approaches based on historical operational data; however, no algorithm was universally superior, as reported performance depended on the dataset, target variable, operational context, and validation procedure. Most studies focused on prediction, monitoring, fault diagnosis, and operational optimisation, while reinforcement learning, hybrid mechanistic–data-driven models, adaptive control, and sustained industrial deployment remained comparatively limited. Sustainability assessments emphasised operational and economic outcomes more frequently than social, ethical, circular-economy, and broader environmental implications. Geographical results reflected the locations assigned to the reviewed studies and industrial cases rather than regional AI maturity. The review identifies data availability, heterogeneous validation practices, model transferability, and limited evidence of sustained industrial deployment as recurring challenges. It proposes a staged research agenda to develop more reliable, transferable, and human-supervised AI applications across copper-processing operations. Full article

Introduction: Transparency and public reporting have become central components of quality governance in cardiothoracic surgery, promoting professional accountability, supporting informed patient decision-making, and strengthening public trust. Their importance is particularly evident in a specialty characterized by technically demanding procedures and high clinical risk. Aims and Objectives: This narrative review examines the impact of public reporting of cardiothoracic surgical outcomes on clinical practice, ethical decision-making, surgeon well-being, and patient choice. It explores both the intended benefits of transparency and its potential unintended consequences. Unlike previous reviews focusing primarily on quality metrics or reporting systems, this review integrates clinical evidence with ethical analysis to propose a balanced framework for responsible transparency in cardiothoracic surgery. Materials and Methods: A narrative literature review was conducted using the PubMed and ScienceDirect databases. Relevant articles published between 2000 and 2026 were identified using the search terms “cardiothoracic surgery,” “public reporting,” “transparency,” and “risk adjustment.” Results: Public reporting has been associated with improved benchmarking, enhanced quality improvement initiatives, greater institutional accountability, and more informed patient choice. Evidence also suggests modest improvements in selected clinical outcomes. However, these benefits may be accompanied by unintended effects, including risk-averse decision-making, avoidance of high-risk cases, distortion of clinical judgment, reduced willingness to adopt innovative techniques, and potential inequities for institutions caring for more complex patient populations. Conclusions: Public reporting should evolve beyond the simple disclosure of performance metrics toward an ethically grounded model of transparency that is context-sensitive, appropriately risk-adjusted, and patient-centered. Such an approach should promote shared accountability, emphasize continuous quality improvement rather than punitive performance assessment, and incorporate expert clinical interpretation to ensure fairness, preserve innovation, and support equitable patient care. Full article

Artificial intelligence has become increasingly embedded in digital marketing, reshaping how firms collect consumer data, personalize content, automate persuasion, and evaluate campaign performance. While these developments offer clear strategic benefits, they also raise important ethical questions concerning privacy, transparency, fairness, manipulation, and accountability within platform-mediated marketing environments. This article presents a structured thematic review of recent research on the ethics of AI-driven digital marketing. The review focuses on English-language peer-reviewed journal articles and high-quality conference proceedings published between January 2023 and September 2025. Searches were conducted across Scopus, ScienceDirect, SpringerLink, ACM Digital Library, IEEE Xplore, MDPI, PubMed, and selected academic repositories. After deduplication, screening, and full-text assessment, 91 studies were included in the final synthesis. Methodological quality was appraised using the Mixed Methods Appraisal Tool and Joanna Briggs Institute critical appraisal criteria, while the findings were examined through thematic synthesis. The review identifies five recurring ethical domains in the literature: data privacy and GDPR compliance, algorithmic transparency and explainable AI, algorithmic fairness in targeting and automated decision-making, dark patterns and deceptive interface design, and influencer or virtual influencer disclosure. The evidence suggests that privacy, consent, deceptive design, and transparency are the most extensively discussed areas, whereas the practical effectiveness of fairness interventions, explainability tools, and disclosure mechanisms remains more mixed and context-dependent. Across these themes, ethical risks appear not only as the result of individual corporate decisions but also as outcomes shaped by platform infrastructures, ranking systems, data access arrangements, and performance-oriented advertising metrics. The article contributes by organizing recent scholarship into a coherent thematic framework and by situating AI-driven digital marketing ethics within a broader platform-governance perspective. It argues that responsible implementation requires clearer distribution of accountability among businesses, platforms, regulators, and researchers. The review is limited by its focus on the 2023–2025 period, its English-language scope, and the methodological heterogeneity of the included studies, which prevents statistical meta-analysis. Full article

This study examines the role of the ethics of responsibility at individual, societal, and institutional levels and explores its potential to address the ethical challenges of the contemporary world. This ethical framework requires individuals and institutions to consider not only their own interests but also the long-term societal and environmental consequences of their decisions. The study explores how this approach provides guidance in the contexts of technological innovations, environmental crises, and globalization. Technological developments are presented as a domain that both expands the scope of ethics of responsibility and introduces new ethical challenges. It is emphasized that artificial intelligence (AI) systems may give rise to issues such as bias, data privacy violations, and the spread of misinformation through digital platforms. In this context, the necessity of designing “fair AI” and ensuring that digital platforms operate in alignment with ethical principles is emphasized. From the perspective of environmental sustainability, it is stated that while individual efforts, such as recycling and energy conservation, are important, institutions must focus on large-scale initiatives, such as carbon-neutral targets and circular economy models. The article also addresses the criticisms and challenges associated with implementing this framework. The subjective nature of ethical principles and the conflict between diverse cultural values make the universal adoption of this approach difficult. Additionally, the prioritization of individual interests within the capitalist system and the inadequacy of global cooperation are seen as major obstacles to the practical application of ethics of responsibility. This situation underscores the importance of both individual awareness and institutional policies. In conclusion, this framework is presented as an important guide capable of addressing the complex issues of the contemporary world. It is argued that the broader application of this approach is essential in areas such as technological advancements, environmental crises, and global inequalities. Fulfilling the ethical responsibilities of individuals, institutions, and the international community is critical for creating a more equitable and sustainable world. Full article

Corporate sustainability has traditionally been evaluated through environmental, social, and economic dimensions. However, increasing sustainability challenges, stakeholder expectations, and regulatory requirements indicate that sustainability extends beyond conventional performance measures and requires an integrated perspective that considers both internal organizational capabilities and external institutional conditions. This study develops an integrated internal–external sustainability framework and prioritizes sustainability dimensions from the perspective of licensed sustainability auditors in Türkiye. The proposed framework comprises governance, ethical, behavioral, cultural, environmental, social, economic, legal, and technological dimensions. The Analytic Hierarchy Process (AHP) was applied to pairwise comparison data collected from 15 licensed sustainability auditors to determine the relative importance of these dimensions. The results indicate that external sustainability dimensions received substantially higher priority (70.97%) than internal dimensions (29.03%), demonstrating that sustainability assessments are primarily shaped by environmental challenges, regulatory requirements, stakeholder expectations, technological developments, and market conditions. Among the nine corporate sustainability dimensions, environmental sustainability received the highest overall priority, followed by technological, ethical, social, economic, legal, governance, cultural, and behavioral dimensions. These findings suggest that although internal organizational capabilities remain important, corporate sustainability is predominantly influenced by external institutional and environmental factors. By proposing an integrated internal–external sustainability framework, this study extends conventional sustainability assessment approaches and contributes to the sustainability governance and assurance literature. The findings also provide practical implications for sustainability reporting, assurance, governance, and strategic decision-making by identifying the sustainability dimensions that deserve the greatest managerial and regulatory attention. Full article

Artificial Intelligence (AI) has evolved from a tool for automation into a strategic component of organizational decision-making. However, the extent to which the dimensions of Human–AI Interaction are associated with leadership remains underexplored, particularly in emerging economies. This study examines how interaction quality, productivity enhancement, user experience, organizational impact, ethical governance, and innovation are associated with Leadership Effectiveness and Organizational Sustainability in Colombian organizations. We applied Structural Equation Modeling (SEM) to data collected from 170 participants using a purpose-built 30-item instrument designed to measure eight dimensions of the human–AI relationship through a five-point Likert scale. Six dimensions assessed Human–AI Interaction (Interaction Quality, Productivity and Efficiency, User Experience and Acceptance, Organizational Impact, Ethical Governance, and Innovation and Transformation), while two dimensions assessed leadership (Leadership Effectiveness and Organizational Sustainability). Estimation used maximum likelihood (ML/FIML) as the primary method, with robust ML (MLR) and an item-level WLSMV estimator as sensitivity checks. Correlation analysis (Pearson, with Spearman as a robustness check) revealed consistent positive and significant associations among all construct indicators. Confirmatory Factor Analysis (CFA) confirmed convergent validity, with innovation ($\lambda =0.88$) emerging as the highest-loading dimension. The structural model demonstrated a significant association between Human–AI Interaction and leadership (two-parcel model: $\beta =0.95$, $R^2=0.91$). Under a more conservative six-item leadership specification, the association attenuates to $\beta =0.87$ ($R^2=0.75$), which we treat as the substantive estimate. Common-method-variance diagnostics (Harman’s first factor $=48.8\%$; a common latent factor accounting for ≈41% of variance) and an elevated RMSEA (= 0.13) signal the need for expanded measurement models and longitudinal designs to address causality. The findings suggest that ethical governance and innovation-oriented AI interaction are primary correlates of leadership effectiveness in digital organizations operating in emerging economies. Full article

Precision prevention and control of genetic diseases represent a major public health challenge. This paper provides a structured narrative review of advances in molecular diagnostic technologies across the preconception, preimplantation, and prenatal stages over the past five years. In the preconception phase, next-generation sequencing has become central to carrier screening, while long-read sequencing significantly enhances detection capabilities for complex variants. In the preimplantation phase, research has increasingly focused on non-invasive preimplantation genetic testing, leveraging maternal contamination quantification algorithms and deep learning models to address DNA contamination challenges. During the prenatal phase, stratified diagnostic strategies combining chromosomal microarray analysis and whole-exome sequencing have improved the diagnostic evaluation of fetal structural anomalies. Simultaneously, non-invasive prenatal testing is expanding to include microdeletion/duplication and monogenic disease screening, though positive screening results still require invasive diagnostic confirmation. Future trends lie in multi-technology integration, multi-omics data fusion, and artificial intelligence-assisted decision-making, aiming to enhance resolution while balancing health-economic considerations and ethical standards. Full article

Across societies, judges are entrusted with extraordinary authority to shape legal norms, public policy, and individual rights, with decisions that can influence communities and future generations. This social contract rests on expectations of competence, impartiality, and ethical self-regulation. Popular representations often portray judges as detached, stoic, and immune to typical human emotional reactivity and outside influence. However, judges remain subject to the same biological, psychological, and social realities that affect all humans, including stress physiology, neurocognitive load, emotional strain, and the cumulative effects of allostatic burden. Emerging research suggests that these factors may have important implications for judicial well-being, decision-making, and the effective functioning of justice systems. The recent Nauru Declaration, recognizing that the well-being of judges is essential for a properly functioning judiciary, has led to the 2025 United Nations General Assembly proclamation that the 25th July be recognized as an annual International Day for Judicial Well-being. Drawing on literature identified through scholarly databases, this concept article examines evidence relating to stress, burnout, resilience, and well-being within justice systems, with particular emphasis on judges and judicial well-being. We explore the relevance of lifestyle medicine and whole-person approaches to health in supporting judicial functioning and vitality. Framed through the lens of the judiciary’s societal responsibilities and the broader social contract, we contend that judicial well-being is not merely an individual concern, but one with institutional and societal significance. The article argues that well-being within systems of justice matters to society; it examines evidence relating to judicial stress and self-control fatigue, and concludes with solutions-oriented approaches and future directions for promoting judicial well-being and sustainable justice systems. Full article

The rapid expansion of large language models (LLMs) has intensified ethical challenges related to bias, accountability, transparency, and consistency in AI-mediated decision-making. While existing AI ethics principles provide normative guidance, their application to non-deterministic, probabilistic reasoning systems remains problematic due to principle conflicts, ambiguous interpretations, and inconsistent judgments. This study proposes the Cognitive-Reflective Equilibration Model (CREM), a novel ethical decision-making framework that integrates Piaget’s equilibration of cognitive structures with Rawls’s reflective equilibrium methodology, reinterpreting these humanistic theories as a structured ethical reasoning procedure. The model is developed in two stages: a conceptually grounded framework and an operational 20-step procedure (OCREM) executable within contemporary LLM architectures. As a proof of concept, CREM was tested across five LLMs—ChatGPT, Claude, Gemini, LLaMA, and DeepSeek—using 20 ethical dilemma scenarios, generating 4000 request–response pairs. LLM-based evaluation confirmed the procedural validity of the model—its technical executability, internal consistency, and cross-platform compatibility—rather than the ethical validity of its outcomes. To address this limitation, a supplementary evaluation by a small interdisciplinary panel of five human experts provided preliminary external evidence consistent with the LLM-based procedural findings, with more conservative ratings: the validity of the ethical judgments was rated significantly above the scale midpoint and was associated with the procedural indicators. These results indicate that the procedural quality of CREM executions was associated with expert-rated ethical acceptability. However, the absence of a baseline condition and the single-source ratings preclude causal interpretation. Generalizability across diverse ethical domains and cultural contexts remains to be investigated. Full article

Artificial intelligence is increasingly used to support clinical decision making, yet concerns remain regarding algorithmic aversion, automation bias and the preservation of meaningful human oversight; while explainable AI aims to improve transparency, less attention has been devoted to the design of human–AI interaction protocols. This study investigates Frictional AI, an interaction paradigm that introduces cognitive friction to encourage critical engagement with AI recommendations. First, semi-structured interviews were conducted with a legal expert and a psychologist and analyzed through thematic analysis to identify legal, ethical, and cognitive requirements for AI-assisted decision support. Second, a user study involving 96 medical residents compared three interaction protocols: a conventional explainable AI-first design (XAI) and two friction-based protocols, namely a judicial protocol based on juxtaposed explanations (Judicial AI, JAI) and an adjunct protocol requiring an initial unsupported decision before AI exposure (AAI). Diagnostic accuracy and confidence, perceived usefulness, completion time, and reliance patterns were evaluated. The interviews highlighted the importance of human-centered explanations, contrastive reasoning, preservation of professional responsibility, and the role of user studies in evaluating human–AI interaction. The quantitative results showed that none of the AI-assisted conditions improved diagnostic accuracy relative to the no-support baseline. However, JAI achieved performance comparable to the baseline, outperforming XAI and AAI, and exhibited the lowest level of over-reliance. Overall findings suggest that the effectiveness of decision-support systems depends not only on model performance and explanation quality but also on interaction design. In conclusion, while preserving diagnostic performance, judicial protocols showed promise in mitigating automation bias and promoting active cognitive engagement in clinical decision support. Full article