Artificial Intelligence and the Didactic Transposition of Knowledge: Implications for Curriculum Development and Knowledge Gatekeeping

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions). Clinical decisions informed by biased models may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities due to differing performance among patient populations. However, while inequitable bias can harm patients in this context, a mindful approach leveraging equitable bias can address underrepresentation of minority groups or rare diseases. Radiologists should also be aware of bias after AI deployment such as automation bias, or a tendency to agree with automated decisions despite contrary evidence. Understanding common sources of imaging AI bias and the consequences of using biased models can guide preventive measures to mitigate its impact. Accordingly, the authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development. The authors review definitions of bias in AI, describe common sources of bias, and present recommendations to guide quality control measures to mitigate the impact of bias in imaging AI. Understanding the terms featured in this article will enable a proactive approach to identifying and mitigating bias in imaging AI. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Rouzrokh and Erickson in this issue.

Artificial intelligence (AI) has become an integral part of modern life, impacting various sectors. While it offers significant benefits, AI also presents safety, ethical and social challenges. This literature review examines these challenges, focusing on safety of data privacy, algorithmic bias, decision reliability, and workforce displacement due to automation. The widespread use of AI requires robust data protection measures to safeguard personal information from breaches and misuse. Algorithmic bias, often unintentional, can lead to discriminatory outcomes. The lack of transparency in some AI systems raises concerns about the trustworthiness of their decisions. Furthermore, AI-driven automation has the potential to displace workers, requiring careful planning and strategies for workforce transition. This review explores the complex interplay between AI's potential and its risks. It examines the ethical implications of data collection and usage, the challenges of ensuring algorithmic fairness, and the socioeconomic impact of automation. Finally, it proposes strategies for responsible AI development and deployment, aiming to maximize benefits while mitigating potential harms. These strategies include promoting AI literacy, developing transparent and accountable AI systems, and fostering a balanced approach to AI adoption.

Increasing concerns have been raised regarding artificial intelligence (AI) bias, and in response, efforts have been made to pursue AI fairness. In this paper, we argue that the idea of structural injustice serves as a helpful framework for clarifying the ethical concerns surrounding AI bias—including the nature of its moral problem and the responsibility for addressing it—and reconceptualizing the approach to pursuing AI fairness. Using AI in health care as a case study, we argue that AI bias is a form of structural injustice that exists when AI systems interact with other social factors to exacerbate existing social inequalities, making some groups of people more vulnerable to undeserved burdens while conferring unearned benefits to others. The goal of AI fairness, understood this way, is to pursue a more just social structure with the development and use of AI systems when appropriate. We further argue that all participating agents in the unjust social structure associated with AI bias bear a shared responsibility to join collective action with the goal of reforming the social structure, and we provide a list of practical recommendations for agents in various social positions to contribute to this collective action.

BackgroundGenerative artificial intelligence (AI) is rapidly transforming healthcare, but its adoption introduces significant ethical and practical challenges. Algorithmic bias, ambiguous liability, lack of transparency, and data privacy risks can undermine patient trust and create health disparities, making their resolution critical for responsible AI integration.ObjectivesThis systematic review analyzes the generative AI landscape in healthcare. Our objectives were to: (1) identify AI applications and their associated ethical and practical challenges; (2) evaluate current data-centric, model-centric, and regulatory solutions; and (3) propose a framework for responsible AI deployment.MethodsFollowing the PRISMA 2020 statement, we conducted a systematic review of PubMed and Google Scholar for articles published between January 2020 and May 2025. A multi-stage screening process yielded 54 articles, which were analyzed using a thematic narrative synthesis.ResultsOur review confirmed AI’s growing integration into medical training, research, and clinical practice. Key challenges identified include systemic bias from non-representative data, unresolved legal liability, the “black box” nature of complex models, and significant data privacy risks. Proposed solutions are multifaceted, spanning technical (e.g., explainable AI), procedural (e.g., stakeholder oversight), and regulatory strategies.DiscussionCurrent solutions are fragmented and face significant implementation barriers. Technical fixes are insufficient without robust governance, clear legal guidelines, and comprehensive professional education. Gaps in global regulatory harmonization and frameworks ill-suited for adaptive AI persist. A multi-layered, socio-technical approach is essential to build trust and ensure the safe, equitable, and ethical deployment of generative AI in healthcare.ConclusionsThe review confirmed that generative AI has a growing integration into medical training, research, and clinical practice. Key challenges identified include systemic bias stemming from non-representative data, unresolved legal liability, the “black box” nature of complex models, and significant data privacy risks. These challenges can undermine patient trust and create health disparities. Proposed solutions are multifaceted, spanning technical (such as explainable AI), procedural (like stakeholder oversight), and regulatory strategies.

Artificial Intelligence (AI) has rapidly transformed various industries, providing significant benefits in automation, decision-making, and efficiency. However, AI also presents numerous risks, including bias, lack of transparency, security vulnerabilities, and regulatory challenges. This study employs a Systematic Literature Review (SLR) approach to identify and categorize key risks associated with AI implementation. The findings indicate that AI risks can be classified into technological, social, and regulatory aspects, each posing unique challenges. Algorithmic bias, privacy concerns, and the lack of global AI governance frameworks highlight the need for more robust risk mitigation strategies. To address these challenges, this study recommends enhancing fairness-aware AI models, strengthening AI governance, and increasing public AI literacy. Future research should focus on improving AI accountability, security measures, and ethical guidelines to ensure responsible AI adoption.

The integration of artificial intelligence (AI) in education raises critical ethical concerns that impact multiple stakeholders, including learners, educators, policymakers, technology developers, and institutions. While AI enhances personalized learning, automates assessments, and optimizes administrative tasks, it also presents challenges related to data privacy, algorithmic bias, and the digital divide. Ethical concerns arise from the potential misuse of learner data, the lack of transparency in AI decision-making, and the risk of overreliance on automation, which may compromise human judgment and creativity in education. Addressing these challenges requires collaboration among stakeholders to establish clear ethical frameworks, regulatory policies, and AI literacy initiatives. A human-centered approach to AI integration, emphasizing fairness, accountability, and transparency, is essential to mitigate risks while maximizing benefits. This chapter explores the ethical dimensions of AI in education and proposes strategies to uphold integrity, inclusivity, and trust in AI-driven learning environments.

Artificial Intelligence (AI) is revolutionizing healthcare by enhancing patient outcomes, reducing costs, and increasing the efficiency of medical professionals. This mini-review explores the diverse applications of AI in healthcare, including disease diagnosis, personalized treatment plans, and patient survival rate predictions. AI technologies such as Machine Learning (ML), deep learning, Natural Language Processing (NLP), and Robotic Process Automation (RPA) are becoming integral to modern healthcare practices. These technologies enable early disease detection, particularly in cases like cancer, by analyzing medical images and patient data, leading to more effective and personalized treatment strategies. Additionally, AI can predict patient outcomes by analyzing large datasets from electronic health records, providing valuable insights that can inform clinical decisions. However, the integration of AI in healthcare also presents significant ethical challenges. Issues such as data privacy, algorithmic bias, lack of transparency, and the potential for increased health inequalities need to be addressed. The World Health Organization (WHO) has provided guidelines emphasizing the ethical use of AI, highlighting the importance of designing AI systems that respect human rights and promote equity. As AI continues to advance, it is crucial to ensure its responsible and transparent use to maximize benefits and minimize risks. This review underscores the transformative potential of AI in healthcare while calling for vigilant ethical considerations to ensure that AI technologies are implemented in a manner that enhances patient care and upholds ethical standards.

The integration of Artificial Intelligence (AI) in higher education has transformed teaching, learning, and administrative processes, offering unprecedented opportunities for personalized learning, predictive analytics, and operational efficiency. While AI enhances educational effectiveness, it introduces complex ethical, privacy, and security challenges that require systematic attention. Algorithmic bias, lack of transparency, and limitations in accountability pose significant ethical dilemmas, potentially affecting fairness and student autonomy. Privacy concerns emerge from large-scale collection, storage, and analysis of sensitive student data, necessitating robust data protection measures, including anonymization and differential privacy. Security vulnerabilities, encompassing cyberattacks, model manipulation, and insider threats, further amplify the risks associated with AI deployment. This chapter examines these multidimensional challenges, highlighting the importance of ethical AI design, privacy-preserving techniques, and comprehensive risk management strategies. Integration of governance frameworks throughout the AI lifecycle ensures responsible, secure, and equitable adoption across diverse educational contexts. Cross-cultural and global perspectives are explored to address variations in regulatory standards, cultural expectations, and institutional capacities, emphasizing the need for adaptable and inclusive AI governance. The chapter concludes by identifying future research directions that advance trustworthy AI adoption in higher education, fostering transparency, accountability, and resilience while safeguarding student rights.

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.

Artificial Intelligence (AI) has emerged as a transformative force in addressing global sustainability challenges, offering innovative solutions for energy optimization, environmental conservation, waste management, and smart urban development. This systematic review critically examines the role of AI in advancing sustainability initiatives by synthesizing peer-reviewed literature published between 2015 and 2024. The study explores how AI-driven technologies, including machine learning, deep learning, and big data analytics, contribute to improving energy efficiency, optimizing resource utilization, and enhancing decision-making processes in sustainability efforts. Key applications include AI-powered smart grids, predictive analytics for climate change mitigation, intelligent waste management systems, and real-time environmental monitoring. Despite its vast potential, the integration of AI into sustainability efforts presents significant challenges. Ethical concerns such as algorithmic bias, lack of transparency in decision-making, and data privacy issues remain critical obstacles. Additionally, high implementation costs, limited interdisciplinary collaboration, and the need for robust policy frameworks hinder the large-scale adoption of AI-driven sustainability solutions. This review underscores the importance of addressing these challenges through inclusive governance, interdisciplinary partnerships, and ethical AI development. Future research should focus on refining AI algorithms for higher accuracy, improving global data-sharing mechanisms, and developing cost-effective AI solutions that are accessible to developing regions. By leveraging AI responsibly and equitably, policymakers, researchers, and industry leaders can drive meaningful progress toward achieving the United Nations' Sustainable Development Goals (SDGs) and fostering a resilient, sustainable future for both present and future generations. Keywords: Artificial Intelligence, Sustainability, Sustainable Development, Energy Optimization, Smart Cities, Machine Learning, Environmental Conservation, Waste Management, Climate Change, Sustainable Development Goals (SDGs).

This discussion paper critically reviews the use of artificial intelligence (AI) in credit risk assessment in development finance institutions (DFIs) in emerging markets. The main goal is to assess the opportunities and ethical challenges of AI driven models for financial inclusion. Using secondary data synthesis, case studies and global policy frameworks, the paper examines how machine learning and alternative data sources can improve predictive accuracy, increase credit access and improve DFI operations. It also points out systemic risks such as algorithmic bias, data privacy violation, lack of transparency and technological dependency. The study suggests that responsible AI adoption requires inclusive data strategies, explainable AI frameworks, regulatory harmonization, local capacity building and improved digital literacy. The paper presents a roadmap for using AI as a tool to create equitable and resilient financial ecosystems in low- and middle-income countries by aligning AI deployment with Sustainable Development Goals and ethical governance.

Advances in artificial intelligence (AI) have transformed various aspects of human resource management (HRM), especially in decision making, operational efficiency, and data analysis. The application of AI in HRM offers opportunities to increase organizational effectiveness, but also raises challenges related to transparency, algorithmic bias, and ethics in workforce management. Therefore, this research was conducted to explore the impact of AI in HRM and how organizations can optimize its use. This research uses a literature review method, by examining various scientific sources related to the implementation of AI in HRM. The study was conducted on articles from academic journals, industry reports, and related publications that discuss the role of AI in the recruitment process, performance evaluation, employee training, and strategic decision making. The research results show that AI can improve HRM efficiency through automation of administrative tasks, predictive analytics, and personalization of the employee experience. However, the main challenges found include algorithmic bias, lack of transparency in AI systems, and legal and regulatory uncertainty. In conclusion, AI has great potential in supporting more effective and data-driven HRM, but its use must be accompanied by policies that ensure transparency, accountability and a balance between automation and the role of humans in decision making. Therefore, a careful approach is needed in adopting AI to ensure maximum benefits without compromising aspects of ethics and fairness.

Background:Artificial intelligence (AI) and machine learning (ML) have significantly advanced medical diagnostics and surgical procedures, particularly in spinal and cranial surgery. Robotic-assisted surgery has emerged as a transformative approach, offering increased precision, reduced intraoperative complications, and improved surgical outcomes. This review examines the effectiveness and reliability of AI in spinal and cranial diagnosis, its integration into robotic-assisted surgical interventions, and the associated ethical concerns.Method:An extensive literature search was search was conducted on different search engines such as PubMed, Google Scholar, and Scopus to find relevant articles.Result:Findings suggest that AI models exhibit high accuracy in detecting spinal and cranial pathologies. ML algorithms contribute to enhanced prognostic assessments and decision-making in neurosurgery. Robotic-assisted surgeries have superior accuracy, lower radiation exposure, and fewer postoperative complications compared to conventional methods. However, challenges such as data biases, lack of transparency in AI decision-making, regulatory hurdles, and the high costs of AI-driven interventions pose significant barriers to widespread adoption. Ethical concerns, including patient privacy, algorithmic bias, and the potential overreliance on AI, must be addressed to ensure responsible integration into clinical practice.Conclusion:Use of AI and machine learning improves the diagnostic outcomes and decreases post op complications in the field of spinal and cranial surgery. But certain challenges such as ethical concerns and technical hurdles should be sorted out with effective planning. Further research is necessary to refine AI-driven interventions, enhance cost-effectiveness, and to make sure ethical and equitable implementation of AI and robotic surgery in neurosurgical care.

Artificial intelligence (AI) has transformed the modern communication landscape by offering various conveniences and efficiencies. However, the use of this technology also raises complex ethical challenges. This study aims to analyze the ethical benefits and risks of using AI in communication and identify necessary mitigation strategies. The method used was a systematic literature review of 20 scientific publications from 2021-2025 that discuss the ethical aspects of AI in various communication contexts. The results show that AI provides significant benefits in personalizing services, increasing the efficiency of business communications, expanding access to education and mental health services, and optimizing the user experience. However, the implementation of AI also presents serious risks related to data privacy and security, algorithmic bias that can lead to discrimination, a lack of transparency in decision-making, and the potential for copyright infringement and the spread of misinformation. To minimize these risks, ethical practices are needed that include transparency and accountability in AI implementation, the development of comprehensive regulations and ethical standards, and increasing public digital literacy. This study concludes that the use of AI in communication must be based on a strong ethical framework involving collaboration between technology developers, policymakers, and the public to ensure this technology provides optimal benefits while protecting the fundamental rights of users.

This study explores the transformative role of Artificial Intelligence (AI) within the context of Regional Innovation Ecosystems (RIVs), Smart Specialisation Strategies (RIS3), and the European Union’s Mission on Adaptation to Climate Change. Building upon ten empirical cases from nine countries in the Baltic Sea region, the research examines AI’s potential to enhance sustainable development, climate resilience, and inter-regional cooperation.While the literature on regional innovation emphasizes governance models, stakeholder engagement, and impact assessment, the specific contributions and limitations of AI within these frameworks remain underexplored. This study aims to fill that gap by analyzing the integration of AI across various components of regional development strategies.AI offers substantial benefits in climate adaptation through enhanced data processing capabilities. It improves climate modeling and forecasting by analyzing vast datasets from satellites, sensors, and historical records, enabling more accurate predictions of extreme weather events and strengthening early warning systems. AI supports the optimization of critical resources such as water, energy, and land, and informs the development of adaptive infrastructure including smart energy grids. It also aids in ecosystem conservation and enhances carbon capture through predictive modeling.In the domain of regional collaboration, AI facilitates partner identification and strategic matchmaking by analyzing regional strengths, competencies, and innovation needs. It supports multilingual knowledge-sharing platforms and provides real-time translation, thus lowering communication barriers and improving cross-border collaboration. AI-driven tools also enhance the Entrepreneurial Discovery Process (EDP) by delivering market trend analysis, identifying niche areas for specialization, and simulating policy outcomes to guide strategic decisions.When embedded in participatory processes, AI augments human-centric approaches by analyzing qualitative data from stakeholder workshops, identifying dominant themes, and offering real-time facilitation support. Furthermore, AI can support the creation of targeted communication materials, helping tailor regional development messages to different audiences.The benefits of AI integration include increased speed and efficiency in innovation processes, greater accuracy in modeling and assessments, enhanced foresight through pattern recognition, optimized resource allocation, and scalable solutions that can address both local and global challenges. AI also helps navigate the complexity inherent in multi-stakeholder ecosystems and climate governance.However, several disadvantages merit attention. High initial costs for infrastructure and skilled personnel can impede adoption, especially in less developed regions. The reliability of AI depends heavily on data quality; biased or incomplete data can lead to flawed outputs. Ethical risks include algorithmic bias, lack of transparency, and the “black box” nature of many AI systems. These concerns call for robust oversight and inclusive governance mechanisms. Additionally, AI cannot replicate human intuition, creativity, or value-based judgment, which are essential in participatory and strategic processes. Risks such as job displacement, cybersecurity threats, and the environmental impact of energy-intensive AI systems also raise critical concerns. Finally, there is a risk of widening the digital divide between technologically advanced and under-resourced regions.In conclusion, AI holds significant potential to accelerate sustainable regional innovation, smart specialisation, and climate adaptation. However, realizing these benefits requires a careful balance between technological advancement and social responsibility. Human agency, ethical oversight, and inclusive governance must remain central to ensure that AI contributes to equitable and resilient regional futures.