Empathy, Ethics and Efficacy: The 3Es of Implementing Artificial Intelligence for Consumer Encounters

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.

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.

The rapid development and deployment of artificial intelligence (AI) technologies have sparked intense public interest and debate. While these innovations promise to revolutionise various aspects of human life, it is crucial to understand the complex emotional responses they elicit from potential adopters and users. Such findings can offer crucial guidance for stakeholders involved in the development, implementation, and governance of AI technologies like OpenAI’s ChatGPT, a large language model (LLM) that garnered significant attention upon its release, enabling more informed decision-making regarding potential challenges and opportunities. While previous studies have employed data-driven approaches towards investigating public reactions to emerging technologies, they often relied on sentiment polarity analysis, which categorises responses as positive or negative. However, this binary approach fails to capture the nuanced emotional landscape surrounding technological adoption. This paper overcomes this limitation by presenting a comprehensive analysis for investigating the emotional landscape surrounding technology adoption by using the launch of ChatGPT as a case study. In particular, a large corpus of social media texts containing references to ChatGPT was compiled. Text mining techniques were applied to extract emotions, capturing a more nuanced and multifaceted representation of public reactions. This approach allows the identification of specific emotions such as excitement, fear, surprise, and frustration, providing deeper insights into user acceptance, integration, and potential adoption of the technology. By analysing this emotional landscape, we aim to provide a more comprehensive understanding of the factors influencing ChatGPT’s reception and potential long-term impact. Furthermore, we employ topic modelling to identify and extract the common themes discussed across the dataset. This additional layer of analysis allows us to understand the specific aspects of ChatGPT driving different emotional responses. By linking emotions to particular topics, we gain a more contextual understanding of public reaction, which can inform decision-making processes in the development, deployment, and regulation of AI technologies.

The rapid advancement of Artificial Intelligence (AI) technology has brought about significant positive changes across society. However, it has also introduced challenges like privacy breaches, data bias, and spreading fake news. In response, several countries, including South Korea, have provided ethical guidelines and policies for AI. Yet, these measures often fall short of keeping pace with the speed and diversity of AI development. To address these issues, this study developed a comprehensive approach by integrating ethical considerations into AI education, covering the entire AI technology development and application process. It involved analyzing previous research on AI and AI ethics education and creating a draft of an integrated AI education program focused on problem-solving based on computational thinking and ethical practicality. The draft was refined and supplemented through two rounds of expert Delphi surveys, ultimately leading to an “Integrated AI Education Program.” This proposed program emphasizes that the knowledge content of AI technology and ethical considerations should not be treated separately but addressed together. It aims to enhance moral and social capacities alongside the experience of thinking like a computer scientist when solving real-life problems. Through this, the program supports the ultimate goal of AI education: to foster computational thinking while providing an educational experience that considers both technology and ethics. This study is expected to advance the discussion on integrating AI ethics into AI education curricula and contribute to developing socially responsible AI developers and users.

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.

Artificial intelligence (AI) can help transform tax administration in developing countries by automating certain functions, pinpointing patterns and irregularities, and forecasting future tax collections. AI can enhance the effectiveness, efficiency, and tax justice in tax administration. This paper discusses the development and deployment of AI in tax administration in developing countries. This paper outlines different AI technologies, the opportunities and challenges of using AI in tax administration, and the possible implications. The paper established that there is an increasing interest in harnessing AI in tax administration in developing countries. The challenges of deploying AI include a lack of quality data, inadequate technical expertise, and a paucity of clear legal and regulatory frameworks to govern the application of AI. The benefits of AI in tax administration were found to encompass increased tax revenue mobilisation and the attainment of sustainable development goals. Reduction in corruption, improved tax compliance, reduced tax avoidance and evasion among other benefits. The paper recommends that policymakers and tax authorities in developing countries improve data quality to support AI adoption, invest in AI research, innovation and development while supporting training in AI as well as the creation of a clear legal and regulatory framework. JEL classifications: H20, H21, H26, O33, K3 Article History: Received: June 22, 2024; Reviewed: August 29, 2024; Accepted: September 17, 2024; Available online: September 23, 2024.

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.

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.

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.

The development of artificial intelligence (AI) technology is often seen as the domain of engineers, but ultimately, it is the everyday consumer who uses it. This means that AI is not just an engineering challenge; it must also be understood from the perspectives of marketing and consumer behavior. In this study, we first use the CiteSpace knowledge mapping tool to analyze research trends and their evolution in the field of AI from a macro perspective. Then, from a micro perspective, we carefully select and review key literature to define the concept of AI, and further provide a comprehensive overview of AI applications in marketing. This includes (1) AI technology and product acceptance factors, (2) Algorithm avoiding and doubts about AI, (3) Industry-spcific application of AI, and (4) Consumer behavior and psychological on AI. Finally, based on existing research findings, we propose future research topics and directions that are necessary for the continued exploration of AI in this context. The insights from this study can inspire new research ideas related to AI for marketing and consumer scholars. Furthermore, it offers an opportunity for business marketers and government policymakers to consider AI not just as a technological issue, but from the perspective of the consumers who will ultimately adopt and interact with these technologies.

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.

In recent years, with the rapid development of Brain-Computer Interface (BCI) and Artificial Intelligence (AI) technologies, a trend of increasing integration between the two has emerged. In some practical applications, BCI has been combined with AI to enhance the overall performance of systems, including usability, user experience, and satisfaction, especially in terms of intelligent capabilities. However, this technological integration also introduces new or exacerbates existing ethical risks, such as neural privacy breaches, cross-domain misuse, and unclear system responsibility attribution. This paper discusses the novel or more severe ethical challenges arising from the fusion of BCI and AI technologies, as well as measures and strategies to address these ethical issues, calling for the establishment of more comprehensive ethical guidelines and governance frameworks. It is hoped that this paper will contribute to a deeper understanding and reflection on the ethical risks and corresponding regulations related to the integration of BCI and AI technologies.

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]

Traditionally, human rights activists gathered evidence about violations of particular individuals' human rights to demand that states change their conduct and adopt measures to prevent further violations. Deploying artificial intelligence as part of the decision-making process creates challenges for activists to detect all sources of harm and demand that states take action to address the harms. Abeba Birhane points out that employing artificial intelligence technology can generate harmful impacts that are either difficult to detect or invisible. If harms remain invisible, then it is difficult for human rights defenders to document them. Equally, it becomes challenging to articulate why the harms in question constitute international human rights law violations. As a result, it is harder for human rights defenders to call on states to take action to safeguard fundamental rights. This article puts forward that individuals can make harms arising from the deployment of artificial intelligence as part of the decision-making process more visible by using the theoretical framework of media ecology. It demonstrates that media ecology can provide an additional tool for human rights activists to detect how using artificial intelligence as part of the decision-making process can undermine the enjoyment of a human right. The article uses the right to mental health as a case study to develop this argument. In order to contextualise the analysis, the article focuses on the employment of artificial intelligence to screen candidates for employment as a case study.

PurposeDrawing upon a circular model proposition, Kantian ethics is employed to explore how ethical considerations within AI translate into concrete actions that prioritize transparency, privacy, inclusivity and equality. Additionally, agency theory is applied to understand the relevance of fairness in the interactions between agents, principals and algorithmic systems, particularly in the creation of value through digital platforms.Design/methodology/approachA review of literature on ethical concerns within the AI ecosystem is conducted, proposing a unifying ethical principle and standards. The circular model for ethics in action is then developed, emphasising the responsible use of AI and its role in capturing and creating social value, ultimately contributing to sustainable organizational outcomes. The model also highlights key drivers that shape the ethical framing of AI, as well as the influence of the institutional context on its adoption and effectiveness.FindingsResponsible use of AI positively affects organizational performance and the digital ecosystem via the psychological mechanism of ethical identity. Ethical standards and regulations are the global requirements for the AI ecosystem that are required for achieving a sustainable digital society.Research limitations/implicationsThis study contributes to a comprehensive understanding of the responsible use of AI’s and its practical and theoretical implications for organizations in the current digital ecosystem. Lack of global understanding, awareness and implementation of ethical practice in the AI ecosystem is not yet developed and practice. Future researchers can design a cross-border ethical framework to overcome these limitations. Organizations targeting to increase responsible digital interactions can benefit from maintaining ethical principles through responsible labourers, leaders and all stakeholders involved in the ecosystem.Practical implicationsThis study offers practical guidance for businesses, policymakers and AI practitioners on the ethical use of AI. It emphasizes the need for robust data governance, a “human-first” approach focusing on privacy and accountability and alignment with ethical standards. Given AI’s global reach, international cooperation and standard-setting are essential to navigate diverse regulatory and cultural contexts. The paper also highlights the importance of ethics education for AI developers and practitioners. Investing in training that integrates technical skills with ethical awareness will help build a responsible AI workforce capable of addressing societal impacts and maintaining public trust.Social implicationsThis study underscores the urgent need for responsible AI adoption, highlighting risks such as bias, lack of transparency and privacy concerns. As AI reshapes work, decision-making and governance, its social impact grows – potentially deepening inequalities if left unchecked. The study calls for explainable, fair and inclusive AI systems guided by ethical frameworks that respect human dignity. A “human-first” approach ensures AI supports-not replaces-human agency. By fostering transparency, accountability and cultural sensitivity, organizations can build public trust, empower diverse communities and contribute to a more equitable digital future. Ethical leadership and inclusive design are essential to avoid reinforcing systemic harms.Originality/valueThis study presents an original approach to integrating ethical considerations into the development and deployment of artificial intelligence, by conceptualizing how transparency, accountability and inclusivity can be embedded throughout the AI ecosystem, fostering trust and responsible innovation. Through a comprehensive examination of ethical principles and requirements, we recommend a set of tools and strategies needed to promote ethical AI practices, mitigate risks and maximize societal benefit. Furthermore, this study serves as a roadmap for building AI systems that prioritize human collaboration and uphold fundamental values in the digital age.