Artificial Intelligence In Practice Research Articles

Ethical AI in Information Technology: Navigating Bias, Privacy, Transparency, and Accountability

The rapid advancement of artificial intelligence (AI) technologies has fundamentally transformed the landscape of information technology (IT), offering unprecedented opportunities for innovation and efficiency. However, these advancements also bring significant ethical challenges, including issues of bias, privacy, transparency, and accountability. This paper explores these ethical challenges and proposes a comprehensive ethical framework for the responsible development and deployment of AI in IT. Through an examination of historical context, current trends, and detailed case studies, the framework aims to provide actionable guidelines to mitigate biases, protect privacy, enhance transparency, and ensure accountability in AI systems. By fostering ethical AI practices, this framework aspires to support the sustainable and equitable advancement of AI technologies, ultimately benefiting society as a whole

Ethical AI in Financial Inclusion: The Role of Algorithmic Fairness on User Satisfaction and Recommendation

This study investigates the impact of artificial intelligence (AI) on financial inclusion satisfaction and recommendation, with a focus on the ethical dimensions and perceived algorithmic fairness. Drawing upon organizational justice theory and the heuristic–systematic model, we examine how algorithm transparency, accountability, and legitimacy influence users’ perceptions of fairness and, subsequently, their satisfaction with and likelihood to recommend AI-driven financial inclusion services. Through a survey-based quantitative analysis of 675 users in China, our results reveal that perceived algorithmic fairness acts as a significant mediating factor between the ethical attributes of AI systems and the user responses. Specifically, higher levels of transparency, accountability, and legitimacy enhance users’ perceptions of fairness, which, in turn, significantly increases both their satisfaction with AI-facilitated financial inclusion services and their likelihood to recommend them. This research contributes to the literature on AI ethics by empirically demonstrating the critical role of transparent, accountable, and legitimate AI practices in fostering positive user outcomes. Moreover, it addresses a significant gap in the understanding of the ethical implications of AI in financial inclusion contexts, offering valuable insights for both researchers and practitioners in this rapidly evolving field.

Towards practical artificial intelligence in Earth sciences

AbstractAlthough Artificial Intelligence (AI) projects are common and desired by many institutions and research teams, there are still relatively few success stories of AI in practical use for the Earth science community. Many AI practitioners in Earth science are trapped in the prototyping stage and their results have not yet been adopted by users. Many scientists are still hesitating to use AI in their research routine. This paper aims to capture the landscape of AI-powered geospatial data sciences by discussing the current and upcoming needs of the Earth and environmental community, such as what practical AI should look like, how to realize practical AI based on the current technical and data restrictions, and the expected outcome of AI projects and their long-term benefits and problems. This paper also discusses unavoidable changes in the near future concerning AI, such as the fast evolution of AI foundation models and AI laws, and how the Earth and environmental community should adapt to these changes. This paper provides an important reference to the geospatial data science community to adjust their research road maps, find best practices, boost the FAIRness (Findable, Accessible, Interoperable, and Reusable) aspects of AI research, and reasonably allocate human and computational resources to increase the practicality and efficiency of Earth AI research.

The use of artificial intelligence for graduate nursing education: An educational evaluation.

With artificial intelligence (AI) rapidly advancing, advanced practice nurses must understand and use it responsibly. Here, we describe an assignment in which Doctor of Nursing Practice (DNP) students learned to use generative text AI. Using our program and course outcomes, developed from the 2021 American Association of Colleges of Nursing (AACN) Essentials competency for DNP students to learn and use AI, we reviewed the literature seeking examples using ChatGPT for the DNP informatics course. No published examples existed to guide us toward infusing a ChatGPT assignment into the course. We developed a novel assignment that included a guide for students on how to use ChatGPT. Students were given time before the assignment to learn the AI/chatbot technology. They were then given the assignment and grading rubric. The assignment was to develop a tool for their current or future practice using ChatGPT. During the course faculty debrief, we learned that few students had questions and the assignment was clear. We also learned that students who sought to develop straightforward, uncomplicated patient tools succeeded with the technology. Those who sought to create something for complex patients had more challenges. Nursing education and practice will be influenced by the increasing prevalence of AI. This manuscript outlines an AI-based assignment for graduate nursing education intended for the students to become familiar with current AI and best practices for patient care. The assignment was well received by students. We plan to use it again in the next course offering.

Navigating the Ethical Terrain of AI in Education: A Systematic Review on Framing Responsible Human-Centered AI Practices

With the rapid development of artificial intelligence (AI) in recent years, there has been an increasing number of studies on integrating AI in various educational contexts, ranging from early childhood to higher education. Although systematic reviews have widely reported the effects of AI on teaching and learning, limited reviews have examined and defined responsible AI in education (AIED). To fill this gap, we conducted a convergent systematic mixed studies review to analyze key themes emerging from primary research. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we searched Scopus and Web of Science and identified 40 empirical studies that satisfied our inclusion criteria. Specifically, we used four criteria for the screening process: (1) the study’s full text was available in English; (2) the study was published before April 10th, 2024 in peer-reviewed journals or conference proceedings; (3) the study was primary research that collected original data and applied qualitative, quantitative, or mixed-methods as the study methodology; and (4) the study had a clear focus on ethical and/or responsible AI in one or multiple educational context(s). Our findings identified essential stakeholders and characteristics of responsible AI in K-20 educational contexts and expanded understanding of responsible human-centered AI (HCAI). We unveiled characteristics vital to HCAI, encompassing Fairness and Equity, Privacy and Security, Non-maleficence and Beneficence, Agency and Autonomy, and Transparency and Intelligibility. In addition, we provided suggestions on how to achieve responsible HCAI via collaborative efforts of stakeholders, including roles of users (e.g., students and educators), developers, researchers, and policy and decision-makers.

AI art education - artificial or intelligent? Transformative pedagogic reflections from three art educators in Singapore

ABSTRACT With growing interest in the role and integration of Artificial Intelligence (AI) and AI pedagogies in transformative art education this paper voices the pedagogic reflections of three art educators in Singapore who have engaged AI in their art provision. It discusses and exemplifies the advantages (intelligent), like interdisciplinary opportunity, enhanced student engagement and increased connectionism, and limitations (artificial), like ethical and moral dilemmas and positioning, of AI integration in three art education settings showcasing how educators and learners engage. Model of pedagogic reflection is used to reveal how the educators provided transformative learning with AI, whilst showing the influence of personal art interests, technique modelling and professional reflections on teaching and making involving AI. Paper contributions include insights into and examples of AI practices and pedagogies (like AI literacy and prompt engineering), that are and could be used in art education, reflections on their successes and limits and commentary on the position of AI integration in the circular economy of art education. The paper is positioned to mobilize educator and student voice as an influential driver in the transformation of art education given ongoing developments in digital education futures.

Assessing the AI Acumen: A study on the knowledge, attitude and behaviour of dental students towards artificial intelligence

The influence of Artificial Intelligence (AI) can be seen in every nook and corner of life. It has become an essential part of human life. Its influence in medical field is also noticeable but in certain aspects utilization of AI in dentistry has not been up to a great extent. This questionnaire-based survey was done to assess the knowledge, attitude and behaviour of dental students about AI. A self-administered closed ended questionnaire consisting of 36 questions was distributed among 3100 interns and post graduate students studying in different parts of Rajasthan. The questionnaire consisted of demographic details of respondents as well as fundamental KAP towards AI.Response rate was 90.32%. The results showed that about 56.75% of respondents had poor knowledge of AI and its application in dentistry. Although their attitude towards AI was fair (48.75%) but their practical experience of using AI was poor (57.89%). There was a significant difference between the KAP of postgraduates when compared to interns (p value < 0.05). Pearsons’s correlation test showed that there was no correlation between knowledge and attitude or knowledge with behaviour as well as attitude and behaviour.There is a lack of awareness about the use of AI in dentistry among dental students. So, there is a need for implementation of practical courses to improve the knowledge as well as practice of AI in dentistry.

LEGAL LIABILITIES OF ARTIFICIAL INTELLIGENCE: AN OVERVIEW

Objective: The main objective of the paper is to study the need for Special Regulatory legislation for Technology especially Artificial intelligence (AI) as well as examine how AI and law work together. The paper evaluates the liability of AI when it violates Human Rights and Data Privacy. The paper evaluates how AI has created new possibilities and had posed challenges by interfering Human Rights and challenging Sustainable Development comparing the developments happening in European Union (EU) with countries like India. The paper also attempts to make the complex debate more comprehensible for those with no expertise in this area. Method: The Comparative research approach was administered by highlighting the EU methods to tackle and address the AI liabilities. Analogy and review of the Product Liability Directive (PLD) which is proposed and published by European Union (EU) for an AI system liability and suggestions of National Institution for Transforming India (NITI) Aayog of India to attain Sustainable National Economic Development. The paper analyses from financial and lucrative perspective, what liability rules would curtail the financial damages and other harms caused by AI systems which affect Sustainable Development. Results: The paper findings suggest that influence factor of AI applications should be included in existing Liability framework and must be consistently updated as AIs are characterised by unpredictability and independence. Both the EU and India NITI Aayog states that regulatory insight must be an ethical reinforcement for AI practice and hence ensure the sustainability of AI technologies. Originality/Value: The recent government initiatives in EU and India are discussed with respect to the AI liability. The study provides positive insights of Artificial Intelligence (AI) technology and its liabilities to achieve Economic Sustainable Development. The key findings of the paper highlight the importance of Salmond’s defined Tortious liability during British Era for AI technologies for protecting Human Rights and also achieve Sustain Development.

Ethical considerations in AI use for SMEs and supply chains: Current challenges and future directions

The integration of Artificial Intelligence (AI) in Small and Medium-sized Enterprises (SMEs) and supply chains has revolutionized operational efficiencies and decision-making processes. This review paper aims to explore the ethical implications of AI adoption in these sectors, identifying current challenges and proposing future directions for ethical AI deployment. Through an extensive review of existing literature, the paper examines key ethical concerns such as data privacy, bias in AI algorithms, transparency, and the socio-economic impact on the workforce. The findings indicate that SMEs face unique ethical challenges due to their limited resources and expertise, which exacerbate issues related to AI implementation. Additionally, supply chains grapple with transparency and accountability, necessitating immediate attention to ensure ethical practices. The review concludes that establishing a robust ethical framework is crucial for guiding AI integration in SMEs and supply chains. It recommends the development of standardized ethical guidelines, enhanced stakeholder engagement, and increased investment in AI literacy and infrastructure. Future research should focus on creating adaptable ethical models that evolve alongside technological advancements and industry needs, ensuring that AI contributes to sustainable and equitable growth. This paper contributes to the ongoing discourse on ethical AI, offering actionable insights for policymakers, business leaders, and researchers dedicated to fostering responsible AI practices in SMEs and supply chains. Keywords: Ethical AI, SMEs (Small and Medium-sized Enterprises), Supply Chains, Algorithmic Bias, Data Privacy, Transparency, Accountability, AI Trust, Regulatory Compliance, Stakeholder Engagement, Job Displacement, Explainable AI (XAI), Workforce Reskilling, Sustainable AI Practices, AI Ethics Frameworks.

Knowledge, attitude, and practice of artificial intelligence applications in medicine among physicians in Sudan: a national cross-sectional survey.

Artificial intelligence (AI) has emerged as a rapidly developing tool within the medical landscape, globally aiding in diagnosis and healthcare management. However, its integration within healthcare systems remains varied across different regions. In Sudan, there exists a burgeoning interest in AI potential applications within medicine. This study aims to evaluate the knowledge, attitudes, and practices of AI applications in medicine among physicians in Sudan. The authors conducted a web-based survey cross-sectional analytical study using an online questionnaire-based survey regarding demographic details, knowledge, attitudes, and practice of AI distributing through various e-mail listings and social media platforms. A sample of 825 Physicians including doctors in Sudan with different ranks and specialties were selected using the convenient non-probability sampling technique. Out of 825 Physicians, 666 (80.7%) of Physicians have previous knowledge about AI. However, only a small number 123 (14.9%) were taught about AI during their time in medical school, even fewer, just 120 (14.5%) had AI-related lessons in their training program. Regarding attitude, 675 (81.8%) agree that AI is very important in medicine, almost the same number, 681 (82.6%) support the idea of teaching AI in medical schools. Practically, 535 (64.8%) of doctors, think that should get special training in using AI tools in healthcare. Excitingly 651 (78.9%) of physicians are interested in working with AI in future. Based on different ranks of doctors toward AI; Medical Officers exhibited the highest proportion at (32.7%) of knowledge and understanding of AI concepts, followed by House Officers at (16.7%) (p=0.076); regarding attitude, Medical Officers demonstrated the highest (31.6%) favorable attitude, followed by House Officers at (17.5%) (p=0.229); In practice also, Medical Officer showed the highest portion (28.0%) among participants (p=0.129). While there is a positive attitude and some level of AI practice, there remains a considerable gap in knowledge that needs addressing.

ChatGPT in Higher Education: Practical Ideas for Addressing Artificial Intelligence in Nursing Education.

As the use of artificial intelligence (AI) becomes more prevalent in academic settings, there is a growing concern about maintaining a culture of integrity. This article explores the role of academic institutions and programs in fostering a culture of integrity in relation to AI. By implementing specific policies, integrating tools, and utilizing software for AI detection, academic institutions can establish a culture of integrity in relation to AI. These collective efforts foster an environment where ethical AI practices are upheld and reinforce the importance of academic honesty, particularly in the nursing profession. Academic institutions have the capacity to establish integrity-focused policies and integrate anti-AI agent tools in courses to mitigate unethical AI usage, while software advancements assist faculty in identifying AI presence during assessments. Emphasizing the interplay between academic and professional integrity strengthens nurses' dedication to academic honesty. [J Nurs Educ. 2024;63(X):XXX-XXX.].

The Impact of Artificial Intelligence on Corporate Governance

The advent of artificial intelligence (AI) marks a pivotal shift in the landscape of corporate governance, catalyzing a reevaluation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management, and ethical considerations. This study explores the multifaceted impact of AI on corporate governance, offering a nuanced analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. The research underscores the potential of AI to enhance decision-making processes, optimize operational efficiencies, and foster innovation by providing advanced analytical capabilities and predictive insights. However, it concurrently highlights the emergence of unprecedented challenges, including data privacy concerns, algorithmic bias, and the need for robust regulatory frameworks to mitigate risks associated with AI deployment. The article advocates for a proactive stance in redefining corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical considerations and transparency in AI applications. It calls for a collaborative effort among corporate leaders, policymakers, and stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent risks. The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values. However, it is important to note that the approach and methods used in this study are based on a qualitativeliterature review and, therefore, the generalization of the findings across different sectors and corporate governance frameworks may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up with emerging trends and potential risks.

Unleashing the Potential of Artificial Intelligence: Advancements, Applications, and Ethical Considerations

Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various sectors of society. This qualitative study explores the advancements, applications, and ethical considerations surrounding the use of AI. Through in-depth interviews, focus group discussions, and document analysis, the research investigates the latest developments in AI technology, including machine learning, natural language processing, and computer vision. The study examines the diverse applications of AI across industries such as healthcare, finance, transportation, and education, highlighting its role in streamlining processes, enhancing decision-making, and driving innovation. Furthermore, the research delves into the ethical implications of AI adoption, addressing concerns related to privacy, bias, transparency, and accountability. By engaging with stakeholders from academia, industry, and civil society, the study explores perspectives on responsible AI development and governance frameworks. It also discusses initiatives aimed at promoting ethical AI practices, such as fairness, accountability, and transparency in algorithmic decision-making. The findings underscore the need for a balanced approach to AI deployment that maximizes its benefits while mitigating potential risks and societal harms. Key themes identified include the importance of interdisciplinary collaboration, regulatory oversight, and public engagement in shaping the future trajectory of AI. By unpacking the complexities of AI advancements, applications, and ethical considerations, this study contributes to the ongoing discourse on harnessing AI's potential for the benefit of humanity.

ECONOMIC TRANSFORMATION OF DATA ANALYTICS THROUGH AI: EMERGING OPPORTUNITIES AND CHALLENGES IN THE WORKFORCE

The integration of Artificial Intelligence (AI) in data analytics is revolutionizing various industries, driving significant economic transformation, and reshaping the workforce. This study explores the multifaceted impact of AI-driven data analytics, highlighting both the promising opportunities and formidable challenges it presents. Key findings demonstrate that AI significantly enhances data processing capabilities, leading to improved decision-making and operational efficiencies. Furthermore, the emergence of new job roles such as data scientists, AI specialists, and machine learning engineers underscores the demand for specialized skills. However, the rapid adoption of AI also exposes considerable skill gaps in the workforce and raises ethical concerns, particularly regarding data privacy, security, and algorithmic bias. Addressing these challenges requires strategic workforce training, robust governance frameworks for ethical AI practices, and effective change management strategies to overcome resistance to change. By comprehensively addressing these issues, businesses and policymakers can harness the full potential of AI in data analytics, fostering innovation, economic growth, and a smooth transition to an AI-driven economy.

Technical debt in AI-enabled systems: On the prevalence, severity, impact, and management strategies for code and architecture

Context:Artificial Intelligence (AI) is pervasive in several application domains and promises to be even more diffused in the next decades. Developing high-quality AI-enabled systems — software systems embedding one or multiple AI components, algorithms, and models — could introduce critical challenges for mitigating specific risks related to the systems’ quality. Such development alone is insufficient to fully address socio-technical consequences and the need for rapid adaptation to evolutionary changes. Recent work proposed the concept of AI technical debt, a potential liability concerned with developing AI-enabled systems whose impact can affect the overall systems’ quality. While the problem of AI technical debt is rapidly gaining the attention of the software engineering research community, scientific knowledge that contributes to understanding and managing the matter is still limited. Objective:In this paper, we leverage the expertise of practitioners to offer useful insights to the research community, aiming to enhance researchers’ awareness about the detection and mitigation of AI technical debt. Our ultimate goal is to empower practitioners by providing them with tools and methods. Additionally, our study sheds light on novel aspects that practitioners might not be fully acquainted with, contributing to a deeper understanding of the subject. Method:We develop a survey study featuring 53 AI practitioners, in which we collect information on the practical prevalence, severity, and impact of AI technical debt issues affecting the code and the architecture other than the strategies applied by practitioners to identify and mitigate them. Results:The key findings of the study reveal the multiple impacts that AI technical debt issues may have on the quality of AI-enabled systems (e.g., the high negative impact that Undeclared consumers has on security, whereas Jumbled Model Architecture can induce the code to be hard to maintain) and the little support practitioners have to deal with them, limited to apply manual effort for identification and refactoring. Conclusion:We conclude the article by distilling lessons learned and actionable insights for researchers.

Uncovering the Issues Associated with AI and other Disruptive Technology Enabled Operational Practices in Healthcare Sectors in India

Background: Advanced technologies, including artificial intelligence (AI) and other Disruptive technology, have been directly responsible for the significant changes, renovations, and enhancements that have taken place in healthcare systems around the world. In spite of the many challenges, particularly in nations still growing their economies, the healthcare industry has a significant number of opportunities. Objective: To explore the key obstacles that were encountered by the healthcare industry both during and after the introduction of AI and other Disruptive technological practices associated with Health 4.0 in the healthcare industry to uncover how these variables influence AI and other Disruptive technology adoption in healthcare sector of India. Methods: An online survey format that included standardized questionnaire data was obtained from 83 hospitals, and a total of 434 samples have been implemented for various healthcare administrative staff members by the adoption of AI and Disruptive technology. ANOVA analysis was done to confirm the hypotheses' assumptions, then descriptive statistics were done to analyze the mean value, and also EFA and CFA analysis with SEM analysis has been done in the SPSS program for numerous validity tests (version 20). Results: This research explored 15 issues that healthcare administration staff members consider barriers and through the use of EFA, only two of the three primary obstacles"Additional workload" and "Problems with adopting technology" have a substantial impact on the rate at which AI is adopted in the healthcare industry, as seen through the eyes of the workers in that area. Conclusion: These challenges include a high need for capital, extra investments in new technologies like the internet of things (IoT), artificial intelligence (AI), business analytics, , resistance to change from both employees and employers, the need for a skilled workforce, and the upkeep of support systems. The use of blockchain technology in India's healthcare system as a secure service for administrative workers in Health 4.0 Practices could solve data security problems.

A review of green artificial intelligence: Towards a more sustainable future

Green artificial intelligence (AI) is more environmentally friendly and inclusive than conventional AI, as it not only produces accurate results without increasing the computational cost but also ensures that any researcher with a laptop can perform high-quality research without the need for costly cloud servers. This paper discusses green AI as a pivotal approach to enhancing the environmental sustainability of AI systems. Described are AI solutions for eco-friendly practices in other fields (green-by AI), strategies for designing energy-efficient machine learning (ML) algorithms and models (green-in AI), and tools for accurately measuring and optimizing energy consumption. Also examined are the role of regulations in promoting green AI and future directions for sustainable ML. Underscored is the importance of aligning AI practices with environmental considerations, fostering a more eco-conscious and energy-efficient future for AI systems.

Enhancing Tourists' Satisfaction: Leveraging Artificial Intelligence in the Tourism Sector

The tourism industry plays a pivotal role in global economic growth and development, significantly contributing to GDP, job creation, and cultural exchange. However, delivering exceptional tourist experiences remains challenging due to factors such as overcrowding, inadequate infrastructure, language barriers, and cultural insensitivity. The integration of artificial intelligence (AI) technologies offers a promising solution to these challenges. AI can enhance personalization, streamline operations, and improve resource allocation, thereby elevating the overall quality of the tourist experience. AI-driven technologies, such as machine learning algorithms, virtual assistants, augmented reality (AR), and virtual reality (VR) applications, can provide tailored recommendations, multilingual support, and immersive experiences. Moreover, AI can enhance safety and security through predictive analytics and real-time monitoring. While the potential of AI to revolutionize the tourism industry is immense, responsible and ethical implementation is crucial to ensure that these innovations benefit all stakeholders and mitigate potential risks. Embracing transparency, privacy, and fairness in AI practices will foster trust and create more inclusive, sustainable, and enriching travel experiences globally.

Unveiling the evolution of generative AI (GAI): a comprehensive and investigative analysis toward LLM models (2021–2024) and beyond

This comprehensive exploration of recent breakthroughs in artificial intelligence (AI) traversed the realms of language models, computer vision, and generative models, unraveling the intricacies of cutting-edge technologies such as GPT-3.5, GPT-4, Pix2Seq, and multimodal models in terms of generative AI. In this multifaceted journey, the focus extended beyond technological prowess to ethical considerations, emphasizing responsible AI practices guided by Google's AI Principles. The nuanced discussions encapsulated the transformative impact of AI on user experiences across various Google products and toolsets, paving the way for a future where natural language interaction, creative content generation, and multimodal understanding redefine human–computer interactions. The research investigation showcased not only the advancements themselves but also the critical lens through which these innovations are approached, underscoring the importance of ethical and responsible AI in shaping the technological landscape.

AI Through Ethical Lenses: A Discourse Analysis of Guidelines for AI in Healthcare

While the technologies that enable Artificial Intelligence (AI) continue to advance rapidly, there are increasing promises regarding AI’s beneficial outputs and concerns about the challenges of human–computer interaction in healthcare. To address these concerns, institutions have increasingly resorted to publishing AI guidelines for healthcare, aiming to align AI with ethical practices. However, guidelines as a form of written language can be analyzed to recognize the reciprocal links between its textual communication and underlying societal ideas. From this perspective, we conducted a discourse analysis to understand how these guidelines construct, articulate, and frame ethics for AI in healthcare. We included eight guidelines and identified three prevalent and interwoven discourses: (1) AI is unavoidable and desirable; (2) AI needs to be guided with (some forms of) principles (3) trust in AI is instrumental and primary. These discourses signal an over-spillage of technical ideals to AI ethics, such as over-optimism and resulting hyper-criticism. This research provides insights into the underlying ideas present in AI guidelines and how guidelines influence the practice and alignment of AI with ethical, legal, and societal values expected to shape AI in healthcare.