AI Ethics: how can information ethics provide a framework to avoid usual conceptual pitfalls? An Overview

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.

With continuing developments in artificial intelligence (AI) and robot technology, ethical issues related to digital humans, AI avatars, intelligent process automation, robots, cyborgs, and autonomous vehicles are emerging, and the need for cultural and social sustainability through AI ethics is increasing. Moreover, as the use of video conferencing and metaverse platforms has increased due to COVID-19, ethics concepts and boundaries related to information and communications technology, cyber etiquette, AI ethics, and robot ethics have become more ambiguous. Because the definitions of ethics domains may be confusing due to the various types of computing platforms available, this paper attempts to classify these ethics domains according to three main platforms: computing devices, intermediary platforms, and physical computing devices. This classification provides a conceptual ethics framework that encompasses computer ethics, information ethics, cyber ethics, robot ethics, and AI ethics. Several examples are provided to clarify the boundaries between the various ethics and platforms. The results of this study can be the educational basis for the sustainability of society on ethical issues according to the development of technology.

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]

Artificial Intelligence (AI) is rapidly changing every aspect of our society—including amplifying our biases. Fairness, trust and ethics are at the core of many of the issues underlying the implications of AI. Despite this, research on AI with relation to fairness, trust and ethics in the information systems (IS) field is still scarce. This panel brought together academia, business and government perspectives to discuss the challenges and identify potential solutions to address such challenges. This panel report presents eight themes based around the discussion of two questions: (1) What are the biggest challenges to designing, implementing and deploying fair, ethical and trustworthy AI?; and (2) What are the biggest challenges to policy and governance for fair, ethical and trustworthy AI? The eight themes are: (1) identifying AI biases; (2) drawing attention to AI biases; (3) addressing AI biases; (4) designing transparent and explainable AI; (5) AI fairness, trust, ethics: old wine in a new bottle?; (6) AI accountability; (7) AI laws, policies, regulations and standards; and (8) frameworks for fair, ethical and trustworthy AI. Based on the results of the panel discussion, we present research questions for each theme to guide future research in the area of human–computer interaction.

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.

Integrating artificial intelligence (AI) into customer engagement practices transforms how organizations interact with consumers and offers enhanced personalization and efficiency. However, this technological evolution introduces significant ethical challenges, including algorithmic bias, data privacy violations, and a potential decline in consumer trust. This research, Algorithms to Authenticity (ATA), investigates the intricate relationship between AI technologies and authentic, ethical engagement strategies. The central idea of the research study is to explore three main questions: 1. How can businesses effectively implement AI technologies to improve customer engagement ethically? 2. What are the ethical dilemmas and potential risks associated with AI-driven customer engagement? 3. How can transparency and authenticity be maintained in AI-driven interactions to foster trust? The study emphasizes the urgent need for businesses to transition from an algorithm-centric model to one that prioritizes authenticity. This research analyzed ethical concerns for maintaining consumer trust and loyalty. The result of the study aims to provide actionable insights to help businesses navigate the ethical challenges posed by AI to reinforce the commitment to ethical standards while enhancing consumer satisfaction. The study's findings advocate for transparency, accountability, and proactive measures mitigating the risks associated with AI deployment. Given the findings, the three key directions of the study are promoting ethical AI implementation, addressing risks associated with algorithmic misuse, and enhancing transparency to foster authentic customer relationships and trust, reinforced by the concept of ATA ensuring ethical customer engagement. The directions guide organizations, researchers, and policymakers toward ethical AI practices.

The integration of artificial intelligence (AI) and telecommunications is revolutionizing approaches to global development by creating scalable and sustainable solutions to critical challenges. This paper explores the synergy between AI technologies—such as machine learning and predictive analytics—and telecommunications infrastructure, including 5G and the Internet of Things (IoT). It highlights the transformative applications of this convergence in healthcare, education, agriculture, and disaster management, showcasing how these innovations enhance access, efficiency, and outcomes. While the potential is vast, the paper also examines key challenges, including infrastructure gaps, data privacy and security concerns, sustainability issues, and ethical considerations in AI deployment. To overcome these barriers, the paper recommends actionable strategies for policymakers, technologists, and stakeholders, emphasizing the importance of equitable access, ethical frameworks, and collaborative innovation. By addressing these challenges and leveraging the combined strengths of AI and telecommunications, this framework aims to accelerate progress toward inclusive and sustainable global development. Keywords: Artificial Intelligence, Telecommunications, Global Development, Sustainability, Digital Divide, Ethical AI.

Abstract: India's fast-moving consumer goods (FMCG) sector is undergoing a seismic transformation driven by artificial intelligence (AI). This book explores how AI is being integrated across every stage of the FMCG value chain—from supply chain optimization and personalized marketing to predictive inventory and dynamic pricing. It analyzes the convergence of technological infrastructure, data-driven consumer insights, and evolving digital retail formats shaping India’s consumption landscape. With a focus on consumer goods ranging from personal care and packaged food to home products and beverages, the text investigates how Indian startups, conglomerates, and retail platforms are leveraging AI for competitive advantage. Each chapter delves into strategic, operational, and socio-economic implications, supported by case studies and future trend projections. The book also addresses ethical concerns, data governance, and policy challenges associated with AI deployment. Designed for professionals, scholars, and policymakers, this volume provides a comprehensive, forward-looking view of India’s AI-driven FMCG ecosystem. Keywords: FMCG, artificial intelligence, India, personalized marketing, consumer behavior, predictive analytics, retail innovation, digital supply chains, algorithmic pricing, AI ethics

This paper argues that the field of artificial intelligence (AI) ethics needs to give more attention to the values and interests of nonhumans such as other biological species and the AI itself. It documents the extent of current attention to nonhumans in AI ethics as found in academic research, statements of ethics principles, and select projects to design, build, apply, and govern AI. It finds that the field of AI ethics gives limited and inconsistent attention to nonhumans, with the main activity being a line of research on the moral status of AI. The paper argues that nonhumans merit moral consideration, meaning that they should be actively valued for their own sake and not ignored or valued just for how they might benefit humans. Finally, it explains implications of moral consideration of nonhumans for AI ethics research and practice, including for the content of AI ethics principles, the selection of AI projects, the accounting of inadvertent effects of AI systems such as via their resource and energy consumption and potentially certain algorithmic biases, and the research challenge of incorporating nonhuman interests and values into AI system design. The paper does not take positions on which nonhumans to morally consider or how to balance the interests and values of humans vs. nonhumans. Instead, the paper makes the more basic argument that the field of AI ethics should move from its current state of affairs, in which nonhumans are usually ignored, to a state in which nonhumans are given more consistent and extensive moral consideration.

The rapid expansion of artificial intelligence (AI) systems has intensified concerns regarding their energy consumption and carbon footprint, raising questions about whether efficiency-focused strategies under the Green AI paradigm are sufficient to ensure system-level environmental sustainability. This study systematically synthesizes empirical evidence on the energy use and carbon emissions of AI systems across their life cycle and develops a conceptual framework to integrate sustainability constraints into AI deployment. A systematic review was conducted in accordance with PRISMA 2020 guidelines and AMSTAR-2 standards, with searches performed in Web of Science, Pubmed and Scopus up to 19 December 2025. Eligible studies quantitatively assessed energy consumption, carbon footprint, greenhouse-gas emissions, or life-cycle impacts associated with AI systems, including training, inference, hardware, and deployment infrastructures. Ten studies met the inclusion criteria. The results show that AI-related environmental impacts are substantial and highly context-dependent, with inference-phase energy demand often matching or exceeding training-related consumption in large-scale deployments. Life-cycle assessments indicate that hardware-related emissions and electricity mix strongly influence total carbon footprints, while efficiency gains are frequently constrained by system-level feedback. These findings suggest that isolated efficiency improvements are insufficient and that sustainable AI requires coordinated, system-level governance embedding energy and carbon constraints into design and operational decision-making.

Artificial intelligence (AI) ethics is undergoing a practical shift towards putting principles into design practices in developing responsible AI. While this practical turn is essential, this paper highlights its potential risk of overly focusing on addressing issues at the level of individual artifacts, which can neglect more profound structural challenges and the need for significant systemic change. Such oversight makes AI ethics lose its strength in addressing some hidden, long-term harms within broader contexts. In this paper, we propose that the reflection on structural issues should be an integral part of AI ethics. To achieve this, we develop a multi-level framework to analyze socio-ethical issues of AI at both the artifact and broader structural levels. This framework can serve as a potentially transformative approach to uncover some unspoken assumptions in current AI ethics discourses and expose some blind spots in AI guidelines, policies, and regulations. Our paper paves the way to develop a practical approach that can effectively integrate this multi-level framework into real-world AI design and policymaking, ultimately bringing about transformative change.

This study is motivated by the growing discourse on artificial intelligence (AI) ethics that has not yet been fully operationalized in practice, particularly in the health and education sectors in the era of generative AI. It aims to synthesize the literature on the operationalization of AI ethics in order to propose a new governance model that is more adaptive and context-sensitive. A structured thematic synthesis was conducted on peer-reviewed studies published between 2020 and 2025. The analysis reveals a central paradox: a strong global consensus on the principles of justice, transparency, accountability, and privacy stands in stark contrast to significant gaps in their implementation. The emerging challenges are highly context-dependent: in the health sector, they center on technical safety and relational accountability, whereas in the education sector they are more closely related to epistemic integrity and equitable access. The advent of generative AI exacerbates these issues by introducing new systemic risks, such as large-scale hallucinations and an increasingly profound crisis of clarity. In response to the limitations of static compliance-based approaches, this study proposes the Adaptive and Layered AI Governance Model (ALAIGoM), a model of adaptive and layered AI governance that integrates operationalized ethical principles, multi-level governance structures, and a continuous ethics life cycle to enable context-sensitive, adaptive oversight. The model is grounded in proactive trust-building, human-centered design, and mechanisms that ensure acceptability. Overall, this study offers a coherent roadmap for reconfiguring AI ethics from a mere normative checklist into an embedded, adaptive, and responsive governance process.

The integration of Artificial Intelligence (AI) technologies into educational environments—particularly through Adaptive Learning Systems (ALS)—has fundamentally transformed the delivery of instruction and personalized learning. These intelligent systems leverage machine learning algorithms to continuously analyze learner data and adapt content, pacing, and pedagogical strategies to individual student needs, leading to enhanced engagement and improved learning outcomes. However, this rapid digital evolution has surfaced critical concerns about the opacity, fairness, and accountability of AI-driven decisions. Often referred to as “black-box” systems, many AI models offer little to no insight into how they arrive at recommendations or interventions, thereby posing challenges related to transparency, interpretability, trust, and ethical governance. This paper argues that to responsibly integrate AI in education, especially in contexts where student data and futures are at stake, it is essential to move beyond system performance and embrace Explainable AI (XAI)—a design philosophy and technical paradigm focused on making AI decision-making understandable to humans. We propose a student-centric framework for XAI in ALS, which places learners and educators at the center of AI design and deployment processes. The framework is informed by interdisciplinary literature and practical case studies in educational technology and AI ethics. It emphasizes three foundational pillars: (1) Human-centered design, which ensures that AI interfaces and feedback mechanisms are accessible and usable by students and educators alike; (2) Stakeholder collaboration, which fosters ongoing dialogue between developers, teachers, learners, ethicists, and policymakers; and (3) Ethical integration, which embeds principles such as fairness, inclusivity, data privacy, and algorithmic accountability into the development lifecycle of adaptive learning tools. The framework also outlines actionable strategies to ensure that explanations provided by AI systems are contextually meaningful, developmentally appropriate, and pedagogically aligned. It considers the differentiated cognitive needs of various learner groups and promotes educational transparency as a right, not a privilege. Ultimately, this student-centric XAI framework aims to cultivate trustworthy, ethical, and pedagogically sound AI systems that not only support learning but also uphold the rights, agency, and dignity of learners in the digital age. By advocating for transparency and ethical accountability, this work contributes to the growing body of research on responsible AI in education and offers practical guidance for institutions aiming to deploy adaptive technologies in equitable and explainable ways.

In response to the COVID-19 crisis, Artificial Intelligence (AI) has been applied to a range of applications in healthcare and public health such as case identification or monitoring of the population. The urgency of the situation should not be to the detriment of considering the ethical implications of such apps. Implementing ethics in medical AI is a complex issue calling for a systems thinking approach engaging diverse representatives of the stakeholders in a consultative process. The participatory engagement aims to gather the different perspectives of the stakeholders about the app in a transparent and inclusive way. In this study, we engaged a group of clinicians, patients, and AI developers in conversations about a fictitious app which was an aggregate of actual COVID-19 apps. The app featured a COVID-19 symptoms monitoring function for both the patient and the clinician, as well as infection clusters tracking for health agencies. Anchored in Soft Systems Methodology and Critical Systems Thinking, participants were asked to map the flow of knowledge between the clinician, the patient, and the AI app system and answer questions about the ethical boundaries of the system. Because data and information are the resource and the product of the AI app, understanding the nature of the information and knowledge exchanged between the different agents of the system can reveal ethical issues. In this study, not only the output of the participatory process was analysed, but the process of the stakeholders’ engagement itself was studied as well. To establish a strong foundation for the implementation of ethics in the AI app, the conversations among stakeholders need to be inclusive, respectful and allow for free and candid dialogues ensuring that the process is transparent for which a systemic intervention is well suited.

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.