Ethical Implications Research Articles

Explainable AI (XAI) in healthcare: Enhancing trust and transparency in critical decision-making

The integration of artificial intelligence (AI) in healthcare is revolutionizing diagnostic and treatment procedures, offering unprecedented accuracy and efficiency. However, the opacity of many advanced AI models, often described as "black boxes," creates challenges in adoption due to concerns around trust, transparency, and interpretability, particularly in high-stakes environments like healthcare. Explainable AI (XAI) addresses these concerns by providing a framework that not only achieves high performance but also offers insight into how decisions are made. This research explores the application of XAI techniques in healthcare, focusing on critical areas such as disease diagnostics, predictive analytics, and personalized treatment recommendations. The study will analyse various XAI methods, including model-agnostic approaches (LIME, SHAP), interpretable deep learning models, and domain-specific applications of XAI. It also evaluates the ethical implications, such as accountability and bias mitigation, and how XAI can foster collaboration between clinicians and AI systems. Ultimately, the goal is to create AI systems that are both powerful and trustworthy, promoting broader adoption in the healthcare sector while ensuring ethical and safe outcomes for patients.

Whisperings From The Ordinary: A Conversational Artificial Intelligence (AI) Approach To Bridging The Human-Object Divide

In a world increasingly dominated by connected devices, one realm remains silent: most inanimate objects that surround us. This stark contrast highlights a critical gap in human-environment interaction, one that the present work aims to bridge through the power of intelligent conversation and shared understanding. Traditional IoT methods, while efficient, raise crucial concerns regarding sustainability. Embedding electronics into every object risk exceeding costs and generating substantial e-waste. This necessitates exploration of alternative paradigms that unlock the communicative potential of everyday items without environmental burden. Enter the confluence of object detection and conversational AI. Object detection, encompassing techniques like image classification, empowers machines to perceive their surroundings and make informed decisions based on identified objects. Conversational AI refers to the ability of computers to engage in a natural, human-like dialogue. By fitting in object detection, conversational AI can transform lifeless objects into active and intelligent participants in our lives, fostering a richer relationship with the spaces we inhabit. This paper proposes an AI-powered mobile application that unlocks the communicative potential of everyday items, transforming them from passive elements into active participants in our lives. We are exploring to enhance domestic routines by streamlining daily tasks with object-initiated reminders, proactive maintenance suggestions, and personalized advice. This research delves into the technical aspects, potential applications, and ethical implications of granting voice to the voiceless.

Data-driven decision-making in public health: The role of advanced statistical models in epidemiology

This paper critically examines the transformative role of data-driven decision-making in public health, focusing on the integration of advanced statistical models in epidemiology. As the volume and complexity of health data increase, leveraging predictive analytics, machine learning, and real-time data integration has become essential for improving public health outcomes. The study explores how these technologies have shifted public health strategies from reactive to proactive approaches, particularly in areas such as disease surveillance, chronic disease management, and health equity. Through comprehensive analysis, the paper identifies key advancements, such as hybrid models that combine traditional epidemiological frameworks with AI, and the integration of multi-modal data sources that enhance predictive accuracy. The findings emphasize the potential of these models to optimize resource allocation, address health disparities, and provide timely interventions. However, challenges such as data quality, algorithmic bias, and the ethical implications of model transparency are highlighted as critical issues requiring ongoing research. The study concludes that for these models to be effectively adopted, there must be a balance between technological innovation and ethical considerations. Recommendations include the need for interdisciplinary collaboration, improved data governance frameworks, and the development of more inclusive models that are generalizable across diverse populations. This research underscores the necessity of combining robust analytical tools with ethical frameworks to enhance the reliability and equity of public health interventions.

The impact of business analytics on healthcare operations: A statistical perspective

This study examines the transformative potential of business analytics within the healthcare sector, highlighting its wide-ranging applications, the challenges it faces, and potential future developments. The primary goal is to evaluate how advanced analytical tools can enhance healthcare outcomes, improve decision-making, and optimize operational efficiency in healthcare organizations. Through an in-depth literature review, this study critically analyzes existing research and real-world case studies to identify the key trends and obstacles associated with implementing business analytics in healthcare. The findings reveal that business analytics plays a crucial role in personalizing patient care, enabling predictive modeling, and streamlining healthcare operations. These tools support more informed decision-making, leading to better patient outcomes and more efficient resource utilization. However, significant challenges hinder the widespread adoption of business analytics, such as difficulties in integrating data across diverse systems, concerns about patient data privacy, and the need for a cultural shift towards data-driven decision-making within healthcare institutions. In conclusion, while integrating business analytics in healthcare presents substantial benefits, it requires a comprehensive and multifaceted approach. This approach should include technological advancements, the establishment of robust data governance frameworks, and significant organizational changes. The study recommends that healthcare organizations invest in training programs to enhance data literacy among healthcare professionals, develop policies to promote interoperability across different data systems, and carefully address the ethical implications of data usage. Future research should focus on the long-term impacts of business analytics in healthcare, with a particular emphasis on the ethical challenges that may arise. Ensuring these powerful tools are used responsibly will be essential to improving healthcare delivery.

Investigating the Linguistic Fingerprint of GPT-4o in Arabic-to-English Translation Using Stylometry

This study explores the linguistic and stylistic characteristics of machine-generated texts, focusing on the output of GPT-4o. Using various natural language processing (NLP) techniques, including word frequency and stopword count analysis, readability and sentence structure metrics, lexical diversity measures, syntactic frequency analysis, and named entity recognition (NER), the research aims to uncover the stylometric fingerprints present in machine-generated content. The results reveal that GPT-4ogenerated texts exhibit moderate lexical diversity and syntactic complexity, with certain chapters reflecting higher readability and more varied sentence structures, while others lean toward simpler linguistic patterns. The findings also highlight thematic variation across chapters, as observed in the distribution of named entities, which contributes to understanding the model’s handling of different contextual content. The research suggests that while GPT-4o maintains a consistent style in its generated text, there are distinguishable characteristics that may serve as indicators of machine authorship. This provides valuable insights for stylometric analysis, authorship attribution, and the identification of machine-generated texts in various contexts. Future research could extend this work by exploring deeper stylometric features, conducting cross-model comparisons, and developing advanced authorship detection algorithms tailored for AI-generated content. Moreover, the ethical implications of stylometric analysis in the context of AI-generated texts warrant further investigation, particularly as machine-generated content becomes increasingly prevalent across different domains.

On Not Speaking

Abstract This article explores the challenges of intercultural dialogue between European and non-European epistemologies through a study of Heidegger’s conversation with a Japanese thinker. It discusses the inherent challenges and potential failures in such dialogues, emphasizing the differences in language and thought systems, and concludes by considering the ethical implications of these dialogues and the necessity of an “ethics of not speaking”.

Societal Perceptions and Acceptance of Virtual Humans: Trust and Ethics across Different Contexts

This article examines public perceptions of virtual humans across various contexts, including social media, business environments, and personal interactions. Using an experimental approach with 371 participants in the United Kingdom, this research explores how the disclosure of virtual human technology influences trust, performance perception, usage likelihood, and overall acceptance. Participants interacted with virtual humans in simulations, initially unaware of their virtual nature, and then completed surveys to capture their perceptions before and after disclosure. The results indicate that trust and acceptance are higher in social media contexts, whereas business and general settings reveal significant negative shifts post-disclosure. Trust emerged as a critical factor influencing overall acceptance, with social media interactions maintaining higher levels of trust and performance perceptions than business environments and general interactions. A qualitative analysis of open-ended responses and follow-up interviews highlights concerns about transparency, security, and the lack of human touch. Participants expressed fears about data exploitation and the ethical implications of virtual human technology, particularly in business and personal settings. This study underscores the importance of ethical guidelines and transparent protocols to enhance the adoption of virtual humans in diverse sectors. These findings offer valuable insights for developers, marketers, and policymakers to optimise virtual human integration while addressing societal apprehensions, ultimately contributing to more effective and ethical deployment of virtual human technologies.

Generative Artificial Intelligence: Challenges and Opportunities for Systems Developers: A Systematic Mapping of Literature

Generative Artificial Intelligence tools have gained increasing prominence in recent years. However, the increasing use of these technologies and the functionalities they offer has sparked discussions about their impact and even raised concerns about the potential replacement of human work by automation carried out by machines. This study proposes a Systematic Literature Review to evaluate the opportunities and challenges that these technologies present to system developers in the current and future technological scenario. Aiming at state-of-the-art research to identify how Generative AIs are being applied in the context of software development and what are the latest trends and innovations in this field and how these innovations affect the opportunities and challenges for system developers. As a result, several studies were found that highlight how Generative AI has provided productivity and systems development optimized solutions in the industry, as well as promoting innovations. Studies also emphasize the need for a balance between the use of AI tools and development carried out by human participation, which must be mediated by common sense. Furthermore, the review will explore the ethical implications associated with the widespread adoption of AI technologies, addressing issues such as data privacy, decision-making transparency, and the responsibility of developers in ensuring that AI applications are used in a way that benefits society. The findings of this review will contribute to a better understanding of how generative AI is reshaping the software development landscape and provide insights for future research and development in this rapidly evolving field.

Epistemic Injustice in the Medical Context: Introduction to Special Issue

ABSTRACT This special issue aims to delve deeper into the manifestations of epistemic injustice within asymmetric relationships, such as those between laypersons and experts (e.g. patients and physicians). We focus on distinguishing between warranted and unwarranted epistemic exclusions, recognizing that some epistemic harms may stem from justified exclusions, thus necessitating consideration of both just and unjust exclusions. Using examples from the medical field, including instances of misdiagnosis based on statistical correlations, we highlight the intricate nature of epistemic exclusions and their ethical implications. While some exclusions may align with established epistemic practices, others arise from unnecessary practices fueled by identity prejudice or power dynamics, thus presenting as instances of epistemic injustice. Additionally, our exploration of epistemic gaps, particularly within complex environments like medicine, illuminates the challenges individuals face in effectively conveying their experiences and accessing pertinent information. While certain forms of epistemic isolation may be warranted, others result from structural barriers hindering knowledge exchange, prompting critical reflection on the ethical dimensions of such practices.

Ethical implications in using robots among older adults living with dementia.

The aging of the world's population due to accelerating demographic shift on all continents is causing increasing pressure worldwide, giving rise to a "crisis of care" or "care wave." The increase in longevity is resulting in an increase in chronic diseases (such as dementia), an increase in care needs to perform the activities of daily living, and situations of isolation and profound loneliness among older adults. These circumstances are opening the debate on the need to use technology, such as robots, to improve the wellbeing of older adults and their caregivers. The aim of this paper is to address the ethical questions in using social and companion robots for people with dementia, such as concerning consent, the replacement of human care, the potential for increased dependency, and the burden on caregivers. Involving older adults and other stakeholders offers the potential to pursue robotics to support older people while also ensuring a strong ethical commitment. The study is a review of high-impact articles on the topic of the use of social and companion robots with older people with dementia.

Ethical Implications of Cybersecurity Breaches and Data Privacy Violations

The increasing reliance on digital technology has led to a surge in cybersecurity breaches and data privacy violations. These incidents raise significant ethical concerns, impacting individuals, organizations, and society as a whole. This paper explores the ethical ramifications of such breaches, focusing on issues like privacy intrusion, potential harm to individuals, and the erosion of trust. It further examines the ethical obligations of organizations in data collection, storage, and security practices. Finally, the paper discusses potential solutions and approaches to mitigate these ethical concerns and promote a more responsible digital ecosystem Keywords:Cybersecurity, Data Privacy, Ethical Implications, Privacy Intrusion, Trust, Data Security

ARTIFICIAL INTELLIGENCE-BASED AUDIT SOFTWARE: TODAY'S REALITIES AND FUTURE VISION

In the current era of big data, traditional audit methods are proving insufficient, exacerbating the risks businesses face. Artificial intelligence-based audit software (AIAS) is emerging as a promising solution to address these challenges. This study aims to comprehensively examine the current state and future potential of AIAS, analyzing the opportunities and challenges arising from the integration of these technologies into audit processes. To achieve this, we investigate the use cases of AIAS across various audit types and sectors, assessing the benefits they offer and the challenges they present. Additionally, by analyzing the global and local pioneers of AIAS, we identify the factors driving the development of these technologies and uncover future trends. This compilation-based study reveals that AIAS has the potential to make audit processes more efficient, effective, and reliable. However, it also emphasizes the need for careful consideration of issues such as data privacy, algorithmic bias, and ethical implications. This study underscores the importance of collaboration among auditors, businesses, and regulators to fully harness the potential of AIAS while minimizing potential risks. It advocates for investing in a continuous learning and adaptation process. Future research should delve deeper into the impact of AIAS across different sectors and develop recommendations to fully capitalize on the potential of these technologies.

In vitro fertilisation: past - present - future

In vitro fertilisation is a clinical biological issue that has immediate ethical and legal implications. It is therefore not possible to deal with just one aspect, separating it from the others, and even the biologist, carrying out his role in the clinical field (in collaboration with the gynaecologist) is obliged to take an in-depth interest in ethical and legal issues, which is not always the case.

Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study.

Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions. We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy. We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user's self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers. We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t196=7.07, P<.001, Cohen d=0.60) or not aware (t298=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t494=-5.49, P<.001, Cohen d=0.36). Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots.

Ethical, Legal, and Social Implications of Gene-Environment Interaction Research.

Many complex disorders are impacted by the interplay of genetic and environmental factors. In gene-environment interactions (GxE), an individual's genetic and epigenetic makeup impacts the response to environmental exposures. Understanding GxE can impact health at the individual, community, and population levels. The rapid expansion of GxE research in biomedical studies for complex diseases raises many unique ethical, legal, and social implications (ELSIs) that have not been extensively explored and addressed. This review article builds on discussions originating from a workshop held by the National Institute of Environmental Health Sciences (NIEHS) and the National Human Genome Research Institute (NHGRI) in January 2022, entitled: "Ethical, Legal, and Social Implications of Gene-Environment Interaction Research." We expand upon multiple key themes to inform broad recommendations and general guidance for addressing some of the most unique and challenging ELSI in GxE research. Key takeaways include strategies and approaches for establishing sustainable community partnerships, incorporating social determinants of health and environmental justice considerations into GxE research, effectively communicating and translating GxE findings, and addressing privacy and discrimination concerns in all GxE research going forward. Additional guidelines, resources, approaches, training, and capacity building are required to further support innovative GxE research and multidisciplinary GxE research teams.

Research trends of digital transformation in healthcare systems: A bibliometric approach

This study aims to guide future research by examining trends and structures in scholarly publications about digital transformation in healthcare. We analyzed English-language, open-access journal articles related to this topic from the Scopus database, irrespective of publication year. Using tools like Microsoft Excel, VOSviewer, and Scopus Analyzer, we found a growing research interest in this area. The most influential article, despite being recent, has been cited 836 times, indicating its impact. Notably, both Western and Eastern countries contribute significantly to this field, with research spanning multiple disciplines, including computer science, medicine, engineering, business, social sciences, and health professions. Our findings can help policymakers allocate resources to impactful research areas, prioritize multidisciplinary collaboration, and promote international partnerships. They also offer insights for technology investment, implementation, and policy decisions. However, this study has limitations. It relied solely on Scopus data and didn’t consider factors like author affiliations. Future research should explore specific collaboration types and the ethical, social, policy, and governance implications of digital transformation in healthcare.

Emerging Technologies and Cyber Ethics: Exploring Future Challenges and Opportunities

In an era marked by the rapid evolution of technology, the fusion of state-of-the-art innovations with cybersecurity has become both a beacon of promise and a source of profound ethical contemplation. This article embarks on a comprehensive exploration of the intricate relationship between emerging technologies and the ethical considerations they evoke, delineating a roadmap for navigating the future challenges and opportunities they present. From the realm of artificial intelligence, where machine learning algorithms hold the promise of revolutionizing cybersecurity defense mechanisms, to the frontier of quantum computing, with its potential to render current encryption standards obsolete, each advancement brings forth a multitude of ethical quandaries. Similarly, blockchain technology, hailed for its potential to enhance data integrity and transparency, also raises concerns regarding privacy and decentralization. The convergence of biotechnology and cybersecurity introduces unprecedented possibilities for biometric authentication and medical data security, yet it also invites scrutiny regarding the ethical use of personal biological data and the potential for biometric surveillance. Moreover, as autonomous systems become increasingly prevalent in cybersecurity operations, questions regarding accountability, transparency, and the potential for autonomous decision-making to align with ethical principles come to the fore.Within this complex landscape, issues such as data privacy, algorithmic biases, digital rights, and the ethical implications of technological proliferation require careful examination. By delving into these ethical dimensions, this article aims to equip stakeholders with the knowledge and insights necessary to navigate the evolving intersection of technology and morality. Ultimately, this exploration serves not only to illuminate the ethical challenges inherent in emerging technologies but also to catalyze a collective effort towards developing ethically conscious approaches to cybersecurity. By fostering a deeper understanding of the ethical implications of technological advancements, we can strive towards a future where innovation and morality coexist harmoniously in the digital age. Keywords: Cybersecurity, Artificial Intelligence, Machine Learning, Quantum Computing, blockchain technology, Biometric authentication

The Convergence of Artificial Intelligence and Privacy: Navigating Innovation with Ethical Considerations

This article explores the complex relationship between artificial intelligence (AI) and privacy. While acknowledging AI's potential benefits, the authors emphasize the ethical implications of its data-driven nature. The article begins by outlining the privacy risks inherent in AI systems, including data breaches, surveillance, and the potential for bias and discrimination. It then delves into ethical considerations surrounding AI development, such as transparency, accountability, and the need to prioritize human values. Various frameworks for balancing innovation with privacy protection are discussed, including Privacy by Design principles and the General Data Protection Regulation (GDPR). It also examine case studies of privacy violations in AI systems, highlighting the real-world consequences of inadequate safeguards. Looking towards the future, the article identifies advancements in privacy-preserving AI technologies as a crucial area of research. It concludes by advocating for a comprehensive approach to AI governance that combines technological innovation with ethical and regulatory strategies, by stressing the importance of proactive measures to mitigate privacy risks and ensure that AI technologies are developed and deployed in a manner that respects.

Software as a Medical Device (SaMD) in Digestive Healthcare: Regulatory Challenges and Ethical Implications.

The growing integration of software in healthcare, particularly the rise of standalone software as a medical device (SaMD), is transforming digestive medicine, a field heavily reliant on medical imaging for both diagnosis and therapeutic interventions. This narrative review aims to explore the impact of SaMD on digestive healthcare, focusing on the evolution of these tools and their regulatory and ethical challenges. Our analysis highlights the exponential growth of SaMD in digestive healthcare, driven by the need for precise diagnostic tools and personalized treatment strategies. This rapid advancement, however, necessitates the parallel development of a robust regulatory framework to ensure SaMDs are transparent and deliver universal clinical benefits without the introduction of bias or harm. In addition, the discussion highlights the importance of adherence to the FAIR principles for data management-findability, accessibility, interoperability, and reusability. However, enhanced accessibility and interoperability require rigorous protocols to ensure compliance with data protection guidelines and adequate data security, both of which are crucial for effective integration of SaMDs into clinical workflows. In conclusion, while SaMDs hold significant promise for improving patients' outcomes in digestive medicine, their successful integration into clinical workflow depends on rigorous data protection protocols and clinical validation. Future directions include the need for adequate clinical and real-world studies to demonstrate that these devices are safe and well-suited to healthcare settings.

Responsible AI Practice in Libraries and Archives

Artificial intelligence (AI) has the potential to positively impact library and archives collections and services—enhancing reference, instruction, metadata creation, recommendations, and more. However, AI also has ethical implications. This paper presents an extensive literature and review analysis that examines AI projects implemented in library and archives settings, asking the following research questions: RQ1: How is artificial intelligence being used in libraries and archives practice? RQ2: What ethical concerns are being identified and addressed during AI implementation in libraries and archives? The results of this literature review show that AI implementation is growing in libraries and archives and that practitioners are using AI for increasingly varied purposes. We found that AI implementation was most common in large, academic libraries. Materials used in AI projects usually involved digitized and born digital text and images, though materials also ranged to include web archives, electronic theses and dissertations (ETDs), and maps. AI was most often used for metadata extraction and reference and research services. Just over half of the papers included in the literature review mentioned ethics or values related issues in their discussions of AI implementation in libraries and archives, and only one-third of all resources discussed ethical issues beyond technical issues of accuracy and human-in-the-loop. Case studies relating to AI in libraries and archives are on the rise, and we expect subsequent discussions of relevant ethics and values to follow suit, particularly growing in the areas of cost considerations, transparency, reliability, policy and guidelines, bias, social justice, user communities, privacy, consent, accessibility, and access. As AI comes into more common usage, it will benefit the library and archives professions to not only consider ethics when implementing local projects, but to publicly discuss these ethical considerations in shared documentation and publications.