Deployment Of Artificial Intelligence Research Articles

Pedagogical Paradigms in the AI Era: Insights from Saudi Educators on the Long-term Implications of AI Integration in Classroom Teaching

This qualitative study explores the perceptions and experiences of Saudi educators regarding the integration of Artificial Intelligence (AI) in classroom teaching. Through semi-structured interviews with a purposively sampled group of teachers, the research investigates initial experiences with AI, anticipated changes in teaching roles, support and training needs, student reactions, and concerns about long-term AI integration. Employing a constructivist grounded theory approach, the study reveals a nuanced understanding of the opportunities and challenges presented by AI in education. Findings indicate a spectrum of initial responses to AI integration, ranging from enthusiasm to apprehension, emphasizing the need for comprehensive professional development and robust support systems. Educators anticipate significant shifts in their pedagogical roles, foreseeing AI as a facilitator of personalized learning but also expressing concerns about its potential to marginalize traditional teaching practices. Student reactions were generally positive, though mixed, highlighting the need for adaptive AI implementation strategies. Concerns about ethical implications and educational equity were prominent, underscoring the necessity of a balanced, ethical approach to AI deployment. The study contributes to the emerging body of literature on AI in education and offers insights for policymakers, curriculum developers, and educational technologists on effectively harnessing AI's potential while addressing its challenges. It calls for ongoing dialogue, professional development, and ethical considerations as AI becomes more integrated into educational systems, ensuring it serves as a supportive component within the pedagogical framework.

A Review of the Ethics of Artificial Intelligence and Its Applications in the United States

This study is focused on the ethics of Artificial Intelligence and its application in the United States, the paper highlights the impact AI has in every sector of the US economy and multiple facets of the technological space and the resultant effect on entities spanning businesses, government, academia, and civil society. There is a need for ethical considerations as these entities are beginning to depend on AI for delivering various crucial tasks, which immensely influence their operations, decision-making, and interactions with each other. The adoption of ethical principles, guidelines, and standards of work is therefore required throughout the entire process of AI development, deployment, and usage to ensure responsible and ethical AI practices. Our discussion explores eleven fundamental 'ethical principles' structured as overarching themes. These encompass Transparency, Justice, Fairness, Equity, Non-Maleficence, Responsibility, Accountability, Privacy, Beneficence, Freedom, Autonomy, Trust, Dignity, Sustainability, and Solidarity. These principles collectively serve as a guiding framework, directing the ethical path for the responsible development, deployment, and utilization of artificial intelligence (AI) technologies across diverse sectors and entities within the United States. The paper also discusses the revolutionary impact of AI applications, such as Machine Learning, and explores various approaches used to implement AI ethics. This examination is crucial to address the growing concerns surrounding the inherent risks associated with the widespread use of artificial intelligence.

AI-Centric secure outer space operations

This article critically assesses the transformative role of Artificial Intelligence (AI) in military operations, focusing on terrestrial warfare and outer space security. It offers four main points of discussion: 1) An evaluation of AI applications in terrestrial warfare, using real-world technologies such as Project Maven and BAE Systems’ Taranis; 2) an examination of AI’s contributions and risks in the field of cybersecurity; 3) an in-depth look at AI’s growing influence in space security, including technical aspects of systems like the U.S. Space-Based Infrared and the European Data Relay System; 4) an analysis of the ethical and policy challenges associated with AI deployment, informed by the author’s viewpoints on the necessity of international regulation. Drawing from various case studies and expert consultations, the article highlights AI’s capabilities in enhancing decision-making and operational efficiency while discussing the ethical and technical complexities it introduces. The study concludes by offering nuanced recommendations for responsibly integrating AI into military strategies and policies, especially those concerning space security. The aim is to inform and guide military professionals and policymakers by providing actionable insights for responsible decision-making in AI-augmented conflict scenarios on Earth and in space.

Adopting artificial intelligence in sustainable business

Artificial intelligence (AI) in sustainable businesses has attracted interest in various industries. However, research on the sustainable adoption of AI is scarce. The present research contributes to this gap by investigating the benefits of AI for sustainable businesses. Qualitative content analysis was used to investigate twenty-five of the largest Finnish enterprises. In the years 2017–2021, AI was employed in twenty of these enterprises, sixteen of which relied on AI to obtain benefits associated with sustainable business practices. Based on the sample, AI can be perceived as a generalizing technology in Finland. Additionally, the strategic significance of AI for enterprises has been discovered to be increasing. The research results indicate that AI deployment was driven primarily by two interlinked aims: 1) optimization and 2) the pursuit of benefits in various dimensions of sustainable business.

How Artificial Intellegence Can Support Veganism: An Exploratory Analysis

Abstract This article explores the potential ways in which artificial intelligence (AI) can support veganism, a lifestyle that aims to promote the protection of animals and also avoids the consumption of animal products for environmental and health reasons. The first part of the article discusses the technical requirements for utilizing AI technologies in the mentioned field. The second part provides an overview of potential use cases, including facilitating consumer change with the help of AI, technologically augmenting undercover investigations in factory farms, raising the efficiency of nongovernment organizations promoting plant-based lifestyles, and so forth. The article acknowledges that the deployment of AI should not happen in a “solutionist” manner, meaning to always consider nontechnical means for achieving desired outcomes. However, it is important for organizations promoting veganism to realize the potential of modern data-driven tools and to merge and share their data to reach common goals.

Battle between ‘Long’ and ‘Short’ Videos: Fragmented Uses, Diversified Purposes, and the Evolution of China’s Copyright Limitation Rules in the AI Era

Abstract The rapid proliferation of artificial intelligence (AI) technology, particularly the deployment of generative AI in the creation of short videos, has exposed the anachronistic and incomplete nature of the copyright limitations and exceptions (L&E) within the Copyright Law of China. The US jurisprudential approach, characterized by the four-factor fair use analysis and the doctrine of transformative use, has been embraced in China by courts as ‘general legal principles’. These principles have been localized within the restrictive framework of the Berne Convention’s three-step test. However, the application of these principles to China’s burgeoning AI and AI-generated content industry, exemplified by the short video sector, reveals a complex landscape. The purposes behind the utilization of copyrighted works are multifaceted, and the intense rivalry between ‘long’ and ‘short’ videos that represent established and emerging giant Internet platform enterprises further muddies the waters in the application of the American ‘purpose transformation’ concept. A better alternative would be to seek guidance from the Copyright Law of Japan. By grounding decisions in pre-existing, enumerated copyright exceptions, systematically categorizing L&E scenarios, and delineating key considerations for various categories, the judiciary can be steered towards a more nuanced and equitable resolution of individual cases and strike a balance between the interest of copyright holders, Internet users, and the emerging Internet platform enterprises.

AI-Enabled Mental Health Assessment and Intervention: Bridging Gaps in Access and Quality of Care.

This paper explores the transformative potential of artificial intelligence (AI) in revolutionizing mental health assessment and intervention, aiming to address critical gaps in both accessibility and quality of care. Mental health disorders pose a global challenge, with a significant portion of the population facing barriers to timely and effective treatment. Traditional mental health care systems often struggle to meet the increasing demand for services, leading to prolonged waiting times and inadequate support for individuals in distress. Moreover, stigmas surrounding mental health further hinder many from seeking help. The advent of AI technologies offers a promising solution to these challenges. Through advanced machine learning algorithms, AI can enhance the accuracy and efficiency of mental health assessments, enabling early detection of disorders and personalized treatment plans. [1] AI-driven chatbots and virtual assistants can provide continuous, accessible support, offering a bridge to care for individuals who may be hesitant or unable to access traditional services. Furthermore, these technologies can help destigmatize mental health discussions by providing a private and non-judgmental space for users. The paper reviews the current landscape of AI applications in mental health, highlighting successful case studies and identifying key ethical considerations. It also delves into the potential pitfalls and challenges associated with AI implementation, emphasizing the importance of maintaining a balance between technological advancement and human-centric care. Ethical considerations, privacy concerns, and the need for ongoing human oversight are crucial elements in ensuring the responsible deployment of AI in mental health contexts. As AI continues to evolve, it is imperative to establish robust frameworks for regulation, accountability, and transparency to safeguard against unintended consequences. The paper concludes by proposing a holistic approach that integrates AI seamlessly into existing mental health care infrastructures, fostering collaboration between technology developers, mental health professionals, and policymakers to create a future where AI contributes significantly to bridging gaps in access and elevating the overall quality of mental health care.

The Impact of Artificial Intelligence on the Future Workforce

This research examines the extent to which Artificial Intelligence (AI) technology could impact different advanced manufacturing (AM) workforce competencies and technicians’ readiness for AI. A sequential explanatory mixed-method approach is applied to understand what knowledge, skills, and abilities of the AM workforce could be replaced or augmented by AI technology, and how the skill profiles of available AM jobs could change in the presence of AI deployment. The first phase of the research applies natural language processing to examine the competency requirements inherent in AI patents and comparing them to the U.S. Department of Labor’s AM Job Competency Model. The second phase builds upon the findings of the first phase through the expert panel review conducted by the focus group interview. The study found that while there is not complete topical overlap between the AI patents and the AM Competency Model, they do reflect each other in some of the key areas such as data, device, information, and system. Also, AI technology has the potential to perform certain activities related to identify, process, and control. Moreover, AI technology functions overlap with a significant proportion of, academic competencies, workplace competencies, and industry competencies, with 76%, 70%, and 93% respectively. These results suggest that although AI has the potential to perform competencies in various AM domains, it does not necessarily imply the replacement of human workers. The integration of AI in these domains will require careful consideration of the roles of both AI and human workers to optimize process efficiency and quality.

ChatGPT in action: Harnessing artificial intelligence potential and addressing ethical challenges in medicine, education, and scientific research.

Artificial intelligence (AI) tools, like OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), hold considerable potential in healthcare, academia, and diverse industries. Evidence demonstrates its capability at a medical student level in standardized tests, suggesting utility in medical education, radiology reporting, genetics research, data optimization, and drafting repetitive texts such as discharge summaries. Nevertheless, these tools should augment, not supplant, human expertise. Despite promising applications, ChatGPT confronts limitations, including critical thinking tasks and generating false references, necessitating stringent cross-verification. Ensuing concerns, such as potential misuse, bias, blind trust, and privacy, underscore the need for transparency, accountability, and clear policies. Evaluations of AI-generated content and preservation of academic integrity are critical. With responsible use, AI can significantly improve healthcare, academia, and industry without compromising integrity and research quality. For effective and ethical AI deployment, collaboration amongst AI developers, researchers, educators, and policymakers is vital. The development of domain-specific tools, guidelines, regulations, and the facilitation of public dialogue must underpin these endeavors to responsibly harness AI's potential.

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges.

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.

Higher Expectations of Artificial Intelligence in Terms of Morality and Humanity

This article delves into the critical question of whether Artificial Intelligence (AI) should adhere to higher standards of morality and humanity. As AI systems infiltrate our everyday lives, their influence surfaces profound ethical and moral conundrums, primarily due to their intrinsic limitation in exercising moral judgments. The ethically sound deployment of AI requires a comprehensive understanding of these moral challenges and the construction of suitable safeguards. The article further scrutinizes the repercussions of AI on our conception of human nature, highlighting potential modifications in human relationships and the essence of our humanity. It contends that AI systems, given their decision-making prowess, should be governed by robust moral principles and human values to curtail harm and endorse fairness and justice. The piece accentuates the pivotal role of human guidance in AI development and application, asserting that these technologies should align with our values. It concludes with an emphasis on the need for continuous surveillance to ensure that AI technologies benefit society, underlining the importance of aligning AI with our moral and human expectations for sustainable societal progression.

Impact of Social Emotional Intelligence on Students' Interpersonal Relationships and Academic Development

The paper explores the intersection of Artificial Intelligence (AI) and mobile homestay design, elucidating AI’s multifaceted role in optimizing space, fostering sustainability, and enhancing user experience within this specific context. Through a comprehensive literature review, theoretical framework development, and detailed case studies, the paper unveils the significant potential and challenges of implementing AI in mobile homestay design. The case studies spotlight AI’s ability to dynamically optimize small spaces, promote sustainable practices, and tailor user experiences, providing invaluable insights for designers and researchers alike. However, alongside its potential, the ethical considerations, including privacy, security, and bias, are scrutinized, emphasizing the necessity for responsible and ethical AI deployment. Furthermore, the review of various AI tools and techniques provides practical insights for practitioners in the field. The paper concludes by highlighting areas for future research, particularly in developing ethical frameworks and exploring diverse AI applications in various mobile homestay contexts, to further understand and leverage the potent synergy between AI and design in crafting innovative, sustainable, and user-centric spaces.

Ethics and biomedical engineering for well-being: a cocreation study of remote services for monitoring and support

The well-being of students and staff directly affects their output and efficiency. This study presents the results of two focus groups conducted in 2022 within a two-phase project led by the Applied Biomedical and Signal Processing Intelligent e-Health Lab, School of Engineering at the University of Warwick, and British Telecom within “The Connected Campus: University of Warwick case study” program. The first phase, by involving staff and students at the University of Warwick, aimed at collecting preliminary information for the subsequent second phase, about the feasibility of the use of Artificial Intelligence and Internet of Things for well-being support on Campus. The main findings of this first phase are interesting technological suggestions from real users. The users helped in the design of the scenarios and in the selection of the key enabling technologies which they considered as the most relevant, useful and acceptable to support and improve well-being on Campus. These results will inform future services to design and implement technologies for monitoring and supporting well-being, such as hybrid, minimal and even intrusive (implantable) solutions. The user-driven co-design of such services, leveraging the use of wearable devices and Artificial Intelligence deployment will increase their acceptability by the users.

Artificial Intelligence in education: What are the opportunities and challenges?

Objective:The paper aims to explore the integration of Artificial Intelligence (AI) tools in the educational sector. It underscores the urgent need for establishing proper regulations and checks to ensure the ethical and effective use of these tools.
 Method:
 
 The insights are based on a think piece authored by UNESCO Assistant Director-General for Education, Stefania Giannini.
 Data was collected through a global survey involving over 450 educational institutions, including schools and universities.
 
 Results:
 
 A mere 10% of the institutions surveyed have formulated policies or guidelines concerning the deployment of generative AI.
 The scrutiny for validating textbooks is found to be more stringent than the introduction of generative AI tools in educational settings.
 Criteria for textbook evaluations encompass content accuracy, relevance to teaching, age suitability, cultural and social appropriateness, and protection against biases.
 
 Conclusions:
 
 The paper suggests that educational institutions should not be solely dependent on AI corporate entities for setting regulations.
 While education continues to be a profoundly human endeavor, the surge of digital tools during the COVID-19 crisis underscored the academic and societal challenges students encounter in the absence of human touch.
 There's a risk that generative AI might diminish the significance of educators and pave the way for increased automation in the educational domain.
 Addressing the prevailing challenges in education necessitates investments in both schools and educators, rather than just technology.
 
 Practical Implications:
 
 It's imperative for education ministries to join forces with other regulatory bodies, particularly those overseeing technological advancements, to assess and approve AI tools.
 UNESCO is at the forefront of global dialogues with stakeholders and is in the process of framing policy guidelines on the use of generative AI in education.
 UNESCO's 'Recommendation on the Ethics of Artificial Intelligence', released in November 2021, accentuates the principles of safety, inclusivity, diversity, transparency, and excellence.

Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery.

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

A novel Edge architecture and solution for detecting concept drift in smart environments

The proliferation of the Internet of Things (IoT), artificial intelligence (AI), the adoption of 5G, and progress towards 6G technology have led to the accumulation of massive amounts of real-world data; however, a significant portion of the data generated by smart cities and smart buildings remains unused. A notable problem is the shift of statistical properties in real-world streaming over time caused by unexpected factors, referred to as concept drift, which results in less efficient predictive models. To address this problem, the latest research leverages the cloud–edge continuum paradigm for the deployment of AI and general smart city applications while utilising the available resources optimally. In this article, we propose a computing architecture for different smart city applications in edge micro data centre (EMDC) settings over a hybrid cloud–edge continuum to support the deployment of AI workloads. We implement a feedback-driven automated concept drift detection and adaptation methodology, combining base learner long short-term memory (LSTM) with Page–Hinkley test (PHT), adaptive windowing (ADWIN) and the Kolmogorov–Smirnov windowing (KSWIN). Real-world data streams are utilised to forecast from various environmental sensors installed at the University of Oulu Smart Campus. The feedback-based concept drift detection and adaption process is first evaluated using synthetic datasets with known concept drift points and then employed in the real-world data. Subsequently, the implementation is evaluated using the state-of-the-art MAE, RMSE, and MAPE methods. The results showed a reduction in MAPE from 8.5% to 3.88% when concept drift detection was applied. Additionally, the challenges faced and the effectiveness of the suggested solutions are explored.

Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance.

The deployment of artificial intelligence (AI) has the potential to make nuclear medicine and medical imaging faster, cheaper, and both more effective and more accessible. This is possible, however, only if clinicians and patients feel that these AI medical devices (AIMDs) are trustworthy. Highlighting the need to ensure health justice by fairly distributing benefits and burdens while respecting individual patients' rights, the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks that arise during the deployment of AIMD: autonomy of patients and clinicians, transparency of clinical performance and limitations, fairness toward marginalized populations, and accountability of physicians and developers. We provide preliminary recommendations for governing these ethical risks to realize the promise of AIMD for patients and populations.

Corpus-based Critical Discourse Analysis on AI Policy: A Comparison Between North America and Developing Countries in East Asia

Artificial Intelligence (AI) has undergone a global transformative impact on people’s lives. However, the realm of empirical research focused on AI within applied linguistics remains limited, predominantly dwelling on qualitative rhetoric and narrative analysis. This research aims to fill this gap by shedding light on the discursive constructions of AI policies between North America and developing countries in East Asia based on corpus data. Two corpora are constructed for quantitative study, utilizing keywords, collocates, and concordance analysis, and two dimensions of dialectical-relational approach are adopted to delve into the results. The findings reveals that both EA and NA prioritize AI-related efforts, but EA emphasizes more on economic and societal applications, while NA highlights governmental sectors’ efforts, cooperation with allies, and national security in AI deployment. The results are closely associated with their social context. This nuanced understanding of AI policy research holds significant implications for policymakers, stakeholders, and scholars actively involved in shaping responsible AI regulations and governance practices on an international scale.

Fairness of artificial intelligence in healthcare: review and recommendations.

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

Creating complexity matrix for classifying artificial intelligence applications in e-commerce: New perspectives on value creation

This research paper provides a comprehensive exploration of the role of Artificial Intelligence (AI) in value creation within the e-commerce sector, focusing on how task and information complexity affect AI deployment. It first outlines the historical development of value theory and value creation, highlighting the shift from traditional modes to modern interactive and co-creation models. Following this, the paper delves into AI’s potential in various e-commerce dimensions including personalization, product recommendation, supply chain efficiency, and more. The centrepiece of the study is a detailed matrix classifying AI into Automated Intelligence, Assisted Intelligence, and Augmented Intelligence, based on the complexity of tasks they execute and the information they analyse. This research study engaged a panel of fifteen industry and academic experts to critically examine and assign complexity scores to various Artificial Intelligence applications within the e-commerce and similar sectors. The experts evaluated task and information complexity, thereby enabling a classification of the applications into a comprehensible matrix. This classification not only provides a guide for AI system design and evaluation but also enhances understanding of their functional dynamics. The paper contributes theoretically by advancing our understanding of AI as a value creator in e-commerce and practically by offering a roadmap for businesses to adopt and leverage AI technologies. As AI continues to revolutionize the e-commerce sector, the findings of this study provide invaluable insights for businesses seeking to gain a competitive advantage in the digital marketplace.