Prospects, Challenges and Implications of Deploying Artificial Intelligence in Tax Administration in Developing Countries

This paper aims to provide a comprehensive review of the integration of artificial intelligence (AI) and blockchain technology in U.S. tax administration. It explores how these technologies are revolutionizing tax compliance and fraud detection, offering a comparative analysis with traditional methods. The paper highlights the potential benefits of these technologies in enhancing efficiency, accuracy, and transparency in tax administration, aligning with the U.S. government's objectives of ensuring fiscal integrity and public trust. The review also examines international best practices and proposes how the U.S. can leverage these technologies to maintain its global leadership in financial governance and innovation. The study is structured around four key objectives: assessing the current integration of AI and blockchain in tax administration, evaluating their effectiveness in enhancing tax compliance, identifying implementation challenges, and developing strategic recommendations. Employing a comprehensive literature review approach, the study synthesizes findings from various sources to provide an in-depth understanding of the role and impact of these technologies in modern tax systems. The results reveal that AI and blockchain significantly improve tax compliance and administration efficiency but also introduce challenges such as data privacy concerns and the need for robust regulatory frameworks. In conclusion, the study underscores the transformative potential of AI and blockchain in tax administration, recommending continuous research and development, coupled with stakeholder education and engagement. These efforts are crucial for overcoming operational challenges and fully harnessing the benefits of these technologies in modernizing tax systems. The paper concludes with strategic recommendations for policymakers, tax authorities, and researchers, emphasizing the importance of a balanced approach that fosters technological innovation while maintaining legal compliance and adherence to fundamental principles. Keywords: Artificial Intelligence, Blockchain, Tax Administration, Tax Compliance, Digital Transformation, Financial Governance.

Die folgenden Auszüge zu KI und Ethik stammen aus einem UNESCO-Dokument, das im Rahmen einer internationalen Konferenz in Peking 2019 entstand. Sie sollen auf die weltweite Bedeutung der Debatte um KI und Ethik aufmerksam machen. BEIJING CONSENSUS ON ARTIFICIAL INTELLIGENCE AND EDUCATION Preamble 1. We, the participants of the International Conference on Artificial Intelligence (AI) and Education, including 50 government ministers and vice ministers, as well as around 500 international representatives from more than 100 Member States, United Nations agencies, academic institutions, civil society and the private sector, met in Beijing, People’s Republic of China, from 16 to 18 May 2019. [...] 2. We reaffirmed the commitment made in the 2030 Agenda for Sustainable Development, particularly Sustainable Development Goal (SDG) 4 and its targets, and discussed the challenges faced by education and training systems in achieving SDG 4. [...] 6. We also recognize the distinctive features of human intelligence. Recalling the principles set forth in the Universal Declaration of Human Rights, we reaffirm UNESCO’s humanistic approach to the use of AI with a view towards protecting human rights and preparing all people with the appropriate values and skills needed for effective human-machine collaboration in life, learning and work, and for sustainable development. 7. We also affirm that the development of AI should be human-controlled and centred on people; that the deployment of AI should be in the service of people to enhance human capacities; [...] and that the impact of AI on people and society should be monitored and evaluated throughout the value chains. [...] AI to Empower Teaching and Teachers 12. Be mindful that while AI provides opportunities to support teachers in their educational and pedagogical responsibilities, human interaction and collaboration between teachers and learners must remain at the core of education. Be aware that teachers cannot be displaced by machines, and ensure that their rights and working conditions are protected. [...] Ensuring ethical, transparent and auditable use of education data and algorithms 28. Be cognizant that AI applications can impose different kinds of bias that are inherent in the data the technology is trained on and uses as input [...]. Be cognizant of the dilemmas of balancing between open access to data and data privacy protection. Be mindful of the legal issues and ethical risks related to data ownership, data privacy and data availability for public goods. [...] 29. Test and adopt emerging AI technologies and tools for ensuring teachers‘ and learners’ data privacy protection and data security. Support robust and long-term study of deeper issues of ethics in AI, ensuring AI is used for good and preventing its harmful applications. Develop comprehensive data protection laws and regulatory frameworks to guarantee the ethical, non-discriminatory, equitable, transparent and auditable use and reuse of learners’ data. 30. Adjust existing regulatory frameworks or adopt new ones to ensure responsible development and use of AI tools for education and learning. Facilitate research on issues related to AI ethics, data privacy and security, and on concerns about AI’s negative impact on human rights and gender equality. https://unesdoc.unesco.org/ark:/48223/pf0000368303 [Zugriff: 21.09.2020]

Rapid integration of Artificial Intelligence (AI) into the military domain necessitates actionable strategies for translating high-level principles of responsible use into practical guidelines. However, there remains a problematic gap between these principles and the norms that govern the use of AI in military operations. Moreover, these norms are highly dependent on the particular context in which military AI is deployed. This leads to normative uncertainty; what is responsible use of AI in a specific military operation? Unclear practical guidelines pose challenges for technology developers and military operators involved in the deployment of military AI. This paper emphasises the need for a context-specific assessment of responsible use of military AI. Moving beyond a one-size-fits-all standard, we propose the Military AI Responsibility Contextualisation (MARC) framework; a structured approach that facilitates a context-specific assessment. In that way, this paper aims to contribute to bridging the gap between abstract principles and practical guidelines. We furthermore emphasise the need for interdisciplinary collaboration in further operationalizing responsible military AI to work towards the ethical and effective development and deployment of AI in military operations.

ABSTRACTDrawing insights from virtue ethics and psychological perspectives on empathy, we propose a framework for integrating empathy, ethics, and efficacy (3E) to guide the responsible development and deployment of artificial intelligence (AI) technologies in consumer service encounters. It encourages developers, vendors, and users to improve efficiencies and productivity but, more importantly, to adopt an empathetic perspective with ethical decision‐making during AI development and deployment. By simultaneously adopting descriptive, analytical, and prescriptive approaches to engage academics, practitioners, and public policymakers, we outline a future research agenda for enriching AI‐consumer service encounters. Beyond conceptual integration, the framework offers practical insights for AI designers, businesses, and regulators by emphasising empathy as a bridge between efficacy and ethics. This perspective supports the development of AI technologies that not only enhance operational effectiveness but also foster consumer trust and well‐being, ensuring AI‐driven services remain human‐centred and ethically sound. A structured decision‐making model demonstrates how AI‐driven services can balance automation with ethical considerations and empathetic engagement, offering a pathway for more responsible AI implementation.

The deployment of artificial intelligence (AI) technologies is proliferating on the African continent, but policy responses are still at their early stages. This article provides an overview of the main elements of AI deployment in Africa, AI's core benefits and challenges in African settings, and AI's core policy dimensions for the continent. It is argued that for AI to build, rather than undermine, socio-economic inclusion in African settings, policymakers need to be cognisant of the following key dimensions: gender equity, cultural and linguistic diversity, and labour market shifts.

The present study investigates the potential impact of artificial intelligence (AI) on the future trajectory of human civilization. It focuses on topics such as super-exponential growth, the potential emergence of a galactic civilization, and the associated "doom" hazards. A significant advancement in machine intelligence with human-like consciousness, strong artificial intelligence (AI), also known as artificial general intelligence (AGI), creates new opportunities and capacities. There's growing anxiety about the risk that weak AI will eventually become strong AI. Every year, new transformer models that are more like human interactions are being created, and we have already witnessed some indications of AGI. It is anticipated that AI will reach a "singularity" and advance on its own without assistance from humans. This thesis explores the theoretical and practical foundations, model building blocks, development processes, challenges, and ethical issues surrounding the creation of Consciousness AI (AGI). This paper examines the meaning of the term "technological singularity," the various types of singularities that have no point of return idea, the philosophical risks associated with the development of AI, and the implications of AI singularity for monetary theory and the new economic order. As a new perspective on the deployment of ethical AI in the face of tremendous technological advancements, the study not only contributes to the theoretical discourse but also explores the possible practical implications of AI on our shared future. Several obstacles to AI advancement are covered in the paper, along with prospective directions for future research.

As new artificial intelligence (AI) tools are being developed and as AI continues to revolutionize healthcare, its potential to advance health equity is increasingly recognized. The 2024 Research Centers in Minority Institutions (RCMI) Consortium National Conference session titled "Artificial Intelligence: Safely, Ethically, and Responsibly" brought together experts from diverse institutions to explore AI's role and challenges in advancing health equity. This report summarizes presentations and discussions from the conference focused on AI's potential and its challenges, particularly algorithmic bias, transparency, and the under-representation of minority groups in AI datasets. Key topics included AI's predictive and generative capabilities in healthcare, ethical governance, and key national initiatives, like AIM-AHEAD. The session highlighted the critical role of RCMI institutions in fostering diverse AI/machine learning research and in developing culturally competent AI tools. Other discussions included AI's capacity to improve patient outcomes, especially for underserved communities, and underscored the necessity for robust ethical standards, a diverse AI and scientific workforce, transparency, and inclusive data practices. The engagement of RCMI institutions is critical to ensure practices in AI development and deployment which prioritize health equity, thus paving the way for a more inclusive AI-driven healthcare system.

Purpose This study analyzes China’s strategic initiatives in metaverse and artificial intelligence (AI) development, examining their impact on academic research, industry innovation, and policy formulation. It aims to understand how government policies and investments have shaped research agendas and to identify challenges and opportunities in these fields. Design/methodology/approach The research employs a comprehensive analysis of government documents, funding schemes, and research output. It examines key policies, investment programs, and academic publications to track trends in metaverse and AI development in China. The study utilizes bibliometric analysis to assess publication trends, citation patterns, and international collaboration networks. Findings China’s proactive approach, characterized by strong government support and significant private sector investment, has led to a substantial increase in research output and quality in metaverse and AI fields. Chinese institutions have become major contributors to global publications, with growing citation rates and presence at international conferences. The research identifies emerging challenges in privacy, ethical AI development, and digital divide concerns. Practical implications The findings provide insights for policymakers, researchers, and industry stakeholders on the development trajectory of metaverse and AI technologies in China. They highlight the need for balanced approaches to innovation, regulation, and ethical considerations in these rapidly evolving fields. Social implications The study underscores the potential of metaverse and AI technologies to transform various sectors of society, from education and healthcare to entertainment and social interactions. It emphasizes the importance of addressing digital equity and ethical AI deployment to ensure broad societal benefits. Originality/value This research offers a comprehensive overview of China’s approach to metaverse and AI development, providing a unique perspective on the interplay between government initiatives, academic research, and industry innovation. It contributes to the broader discussion on the global development of these transformative technologies and their implications for future technological landscapes.

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

Integrating artificial intelligence (AI) into health care offers the potential to address critical challenges related to access to care, workforce burnout, and health inequities. Despite its promise, AI adoption remains limited due to safety, efficacy, and equity concerns. This paper presents a novel and comprehensive framework for responsible AI development, evaluation, and deployment in health care, encompassing four key phases: (1) AI Solution Design and Development, (2) AI Solution Qualification, (3) AI Solution Efficacy and Safety Evaluation, and (4) AI Solution Impact. By establishing rigorous standards for operational, clinical, and technical quality, the framework aims to guide AI developers and health care professionals toward creating AI solutions that are ethical, effective, and scalable. This structured approach fosters collaboration and mitigates risks to help AI achieve its full potential in improving patient outcomes and health care efficiency.

Background: The rapid advancement of artificial intelligence (AI) in various sectors has revolutionized problem-solving approaches, particularly in healthcare. Developed countries have invested significantly in AI research and applications in healthcare, while low-income countries such as Somalia lag due to various challenges. This study aimed to assess the knowledge, attitudes, and practices (KAP) of AI among healthcare professionals in Somalia and explore their familiarity with AI technologies and practices. Methods: A cross-sectional study was conducted from January 1, 2024, to March 15, 2024, among 441 healthcare professionals in Somalia, using an online questionnaire. The questionnaire assessed the participants' sociodemographic information, knowledge of AI applications in healthcare, attitudes towards AI capabilities, and practical experience with AI in healthcare. Results: Most participants demonstrated good knowledge of AI (67.6%) and a positive attitude towards its potential in healthcare (80.5%). However, a significant gap was observed in the practical application of AI, with 79.1% of the respondents reporting poor practice. The study also found that sociodemographic factors such as age, gender, and income level did not significantly influence knowledge or attitudes towards AI but did affect its practical use. Professionals in certain fields such as midwifery and public health are more likely to use AI in their work. Knowledge and attitude scores were also significant predictors of practice scores. Conclusion: Healthcare professionals in Somalia demonstrate a good understanding and positive attitudes towards AI but encounter challenges in its practical application. This study emphasizes the necessity of an enhanced infrastructure, technical expertise, and data access to fully utilize AI's potential in healthcare. It also highlights the significance of addressing ethical considerations and implementing regulations to ensure responsible use of AI in healthcare. Efforts are needed to translate awareness and receptiveness into effective practice, which could result in a better healthcare system.

Purpose This study has proposed a socially responsible approach towards artificial intelligence (AI) deployment and tested a revised AI device use acceptance (RAIDUA)-based conceptual model involving the potential indirect moderating role of consumers’ privacy concerns (PRCO) regarding AI-enabled devices. Design/methodology/approach The newly introduced RAIDUA model was verified with empirical data gathered from 280 respondents. A partial least square-driven methodology was used in the present study. Further, a simple slope analysis was also performed to assess the PRCO for their plausible moderating role. Findings Results confirmed a negative moderation impact of consumers’ PRCO in the interaction between perceived usefulness expectancy and consumers’ perceived emotions. Further moderation analysis confirmed that PRCO weakens the positive interaction between emotions and consumers’ expected willingness and/or desire to practically accept the use of AI-enabled devices, whereas there is a positive moderation effect of consumers’ PRCO in the negative interaction between perceived emotions and objection to the practical use of AI-enabled devices. Social implications This study has proposed AI usefulness expectancy as a socially responsible approach towards ethical AI deployment and tested the RAIDUA model for acceptance and integration of AI-enabled devices. Originality/value This study uniquely integrates AI usefulness expectancy along with perceived AI PRCO as a moderating factor in the RAIDUA model for AI adoption. Both aspects had been overlooked by frequently used Farooq et al.’s (2017) UTAUT-3, Gursoy et al.’s (2019) AIDUA and Salam et al.’s (2025) unified MOOC utilization model frameworks in AI literature. Therefore, we submit that the RAIDUA model offers a novel and original theoretical contribution in technology integration literature and AI adoption-related body of knowledge.

Through Artificial Intelligence (AI), healthcare has brought revolutionary changes to workforce administration by directing talent delegations and reforming operations with IT integration. Healthcare organizations struggle with staff shortages alongside rising operational costs while seeking high-quality patient care which makes AI-driven workforce solutions data-based when addressing these problems. This investigation reveals the ways AI technology brings improved scheduling capabilities along with better talent hiring methods and performance evaluation systems and employee maintenance procedures and implements their integration with health IT infrastructure. This research consists of both systemized review of academic studies and real-world examples and statistical data which reveals how AI automation reduces administrative obstacles while lowering staff issues and generating operational improvements. AI technological capabilities with predictive analytics and machine learning allow for flexible workforce planning and real-time performance tracking together with data-based decision making to create superior business strategies for talent optimization. Artificial intelligence enhances IT integrations which creates better interoperability between Electronic Health Records (EHR) systems as well as workforce management systems thereby optimizing human resource functions while cutting down on processing time. Research evidence demonstrates that AI implementations deliver significant operational improvements which produce enhanced staff performance along with diminished labor expenses and contented employees. AI implementation for healthcare workforce management encounters obstacles because healthcare professionals question its ethics while workforce members avoid adopting changes and the field exhibits technological differences. Future studies need to tackle the present challenges by studying AI governance programs with emphasis on staff flexibility to AI technology integration. The research presents a tactical guide which healthcare institutions can use to optimize workforce management through AI deployment in order to build sustainable operations in transforming digital environments.

BackgroundDespite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness.MethodsSeventeen semi-structured interviews were conducted with birth parents and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI.ResultsThree themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI.ConclusionsThe ethical values that underscore birth parents and mothers’ perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI.

The rise of Artificial Intelligence (AI) technology presents vast transformative possibilities across various sectors, encompassing economic, industrial, social, political, intelligence, and military realms. Consequently, governing the development and deployment of AI has garnered significant attention not only from policymakers and decision-makers but also from the general public. Given AI's potential to shape state power and its dual strategic applications, the governance of AI has become an integral part of global discussions, falling under the purview of cyber diplomacy. This article delineates key issues surrounding AI governance, discusses the evolving role of the EU as a normative force in this arena, and underscores the importance of transatlantic collaboration amid broader global technological competitions.