Ethical AI and Data Integrity: Best Practices and Emerging Concerns

Purpose This study aims to assess the AI preparedness of Indian small and medium enterprises (SMEs) and its impact on social sustainability. This study used a technology–organization–environment-based proposed framework; it explores how technical, organizational and environmental readiness influences ethical practices and social goals. This research investigates whether ethical AI mediates AI readiness and SS in Indian SMEs. Design/methodology/approach The data, using a structure survey, collected from 739 respondents serving SMEs in eight Indian states was used. A five-point Likert scale was used to collect data on all the constructs. Structural equation modeling (SEM) was used to test the theoretical model. Descriptive statistics, reliability and discriminant validity were performed to ensure an effective measurement model. This provided a basis for analysis of direct effects and mediating effects of AI-driven ethical and sustainable business practices. Findings The research revealed positive relationships between AI readiness and social sustainability (β = 0.43) and AI readiness and business ethics (β = 0.51). In addition, business ethics had a strong positive direct effect on social sustainability (β = 0.47). Importantly, business ethics is also an important mediator between AI readiness and social sustainability (indirect effect β = 0.24). Furthermore, it is reflected that ethical integration is important for AI to achieve socially sustainable outcomes. The SMEs are much better prepared to use AI responsibly and inclusively to benefit the stakeholders if they are more digitally and culturally prepared. Research limitations/implications This study is a cross-sectional design that limits the causal inference. Though the responses were taken from the executives of SMEs in eight Indian states, they may have sectoral and regional imbalances. Also, the responses could have personal biases, as respondents may have been influenced by social desirability. Despite these constraints, the research adds to the literature on AI readiness, social sustainability, and the mediating role of ethics. It also highlights avenues for future research, including a longitudinal study on the SMEs and their AI readiness, regional comparisons of SMEs and integration of governing regulations with ethical AI policies in SMEs. Practical implications This research suggests that the SME management must invest in digital tools and promote ethical culture for successful AI use. Ethical auditing may be done by SMEs alongside people analytics to use ethical AI for enterprise social causes. Policymakers can complement initiatives such as IndiaAI and the Business Responsibility and Sustainability Report requirements by including ethical AI training in the SMEs. The responsible adoption of AI can enhance both trust and employee engagement and can potentially improve CSR outcomes. Originality/value The research articulates an “AI-readiness-ethics-sustainability framework” to study AI preparedness and social sustainability in Indian SMEs. Majorly, the literature review reflects the work of researchers on the technical or economic impacts of AI. This study not only explores the technological, organizational and environmental preparedness but also records the mediating effect of ethics on AI readiness of SMEs on social sustainability and advocates that ethically engaged AI can foster efficiency and influence inclusive and sustainable development.

AI ethics is a branch of ethics that focuses on moral principles in the development and use of AI technologies. This research aims to integrate AI ethics in Christian religious education and explore the opportunities and challenges in the process, as well as strategies to overcome these challenges. The method used is a descriptive qualitative approach with literature studies from various sources such as books, national and international journals, and websites. The results show the importance of integrating AI ethics in Christian religious education to shape students' characters, prepare their future, and improve the quality of learning. Challenges faced include limited resources, digital divide, lack of understanding of AI ethics, and the need for adequate supervision and regulation. Strategies to address these challenges include improving technological infrastructure, training for teachers, and developing policies that support the integration of AI ethics in Christian religious education.

Artificial Intelligence (AI) is a driving force leading the intelligent information society. Major advanced countries have established AI into key policy projects and made continuous efforts to nurture and develop future talents through AI education. Unlike conventional software, AI can infer results through training with data, and if there is a data bias, it may cause social and ethical problems. These problems incur extensive damage to society, so ethical consideration is essential in terms of effectiveness and efficiency in implementing AI. Computational thinking aims to perform effective and efficient problem-solving to address real-life problems using computing technology such as AI. Therefore, ethical considerations in AI education can be regarded as an important element of computational thinking. This study aims to analyze the relationship between computational thinking and AI ethical competence from problem-solving using AI. To this end, evaluations and analyses of computational thinking and AI ethical competence were performed based on the evaluation results of the education program with the integration of AI and AI ethics. The analysis demonstrated that the group with relatively high computational thinking skills also showed high AI ethical competence. The findings of this study are expected to facilitate research on nurturing computational thinking through AI-integrated education with sufficient consideration of AI ethics. To increase the effectiveness of the AI-integrated education program, it is necessary to develop a mid-to-long-term education program to systematically examine the process-focused evaluation by systematizing observational and portfolio assessments.

The rapid integration of artificial intelligence (AI) into education necessitates urgent ethical implementation frameworks, especially in vocational systems of developing countries like Indonesia, where workforce readiness meets digital transformation. The purpose of this study is to address gaps in ethical AI governance within Indonesian vocational schools (SMK). A sequential explanatory mixed-methods design was employed, involving 847 stakeholders from 34 provinces and eight in-depth SMK case studies. Findings reveal moderate national AI ethics awareness (M=2.83), with significant regional gaps—from Java (3.01) to Papua (2.34)—and a critical privacy paradox: high concern (M=3.67) coexists with low legal literacy (28.9%). Students actively use AI for academic support (64.3%) but lack institutional guidance, while teachers face identity crises requiring deeper pedagogical reform beyond technical training. The proposed ETIKA-SMK framework, co-developed with experts, received strong validation (relevance 4.3/5.0; innovation 4.4/5.0) and showed improvements in ethical policy compliance (67.8%) and cybersecurity practices (58.7%) at pilot schools. The study highlights the need for differentiated implementation strategies, as 50% of SMKs fall into “Traditional” or “Struggling” categories requiring foundational capacity-building. It concludes that ethical AI integration must be grounded in local contexts, institutional capacities, and cultural values, rather than adopting standardized global models unsuitable for low-resource educational environments.

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.

This chapter examines how Higher Education Institutions (HEIs) are developing governance frameworks to address ethical and responsible deployment of Artificial Intelligence (AI), particularly Generative AI (GenAI). Through a qualitative study of institutional documents, departmental guidelines, and course-level policies at the University of Lethbridge, it identifies key themes such as academic integrity, transparency, and the balancing of ethical considerations with operational efficiency. The establishment and objectives of an AI Policy Working Group (AIPWG) are detailed, highlighting stakeholder collaboration to develop adaptive policies responsive to rapidly advancing AI technologies. Main findings indicate variability in AI policy across academic disciplines, creating potential confusion regarding ethical AI use among students and faculty. The chapter proposes a multitiered governance framework incorporating institutional guidelines aligned with Responsible Management Education (RME) principles—emphasizing ethics, accountability, and sustainability—alongside discipline-specific and course-level policies. It concludes by advocating for international cooperation and continuous stakeholder engagement to refine AI governance, ensuring responsible and ethical integration that aligns with educational values and promotes critical thinking.

Agentic AI for Credit Assessment and Working-Capital Optimization in Supply Chain Finance: A Theoretical Integration of Agency Theory and AI Ethics

Artificial intelligence (AI) is increasingly shaping diverse sectors, including education, sparking debates about its potential to transform pedagogical practices and redefine the role of educators. This study explores the perceptions and applications of generative AI in Moroccan higher education to better understand its implications for teaching and learning. A mixed-methods approach was adopted, combining quantitative data from 130 teachers and 156 students with qualitative insights. Quantitative findings reveal significant differences: students demonstrate greater openness to adopting AI, appreciating its capacity to provide instant feedback, enhance creativity, and improve academic performance. In contrast, teachers express reservations, particularly regarding AI’s potential to undermine critical soft skills such as collaboration, problem-solving, and critical thinking. Qualitative analyses confirm these trends, highlighting that, while AI is perceived as a valuable complementary tool, it cannot replace the essential human role of educators in providing personalized guidance and addressing students’ emotional and cognitive needs. Both groups agree on the necessity of enhanced digital literacy to ensure ethical and effective AI integration. These findings underscore the opportunities of generative AI, such as personalized learning and efficiency, while addressing limitations like ethical concerns and over-reliance, offering actionable insights for policymakers, educators, and technologists aiming to integrate AI responsibly in education.

The article "Navigating AI Adaptation in Organizational Operations: Challenges, Strategies, and Ethical Considerations" delves into the complexities of integrating artificial intelligence (AI) into organizational contexts, highlighting the significant challenges and strategic approaches necessary for successful adaptation. The study emphasizes the critical role of addressing ethical considerations, security risks, and organizational hesitation in effectively implementing AI technologies. Key findings reveal that ethical issues, such as biases in AI algorithms and the need for transparency and accountability, are paramount in building trust and ensuring responsible AI use. Security risks, including data breaches and unauthorized access, must be mitigated through robust cybersecurity measures and data governance frameworks. Additionally, the research underscores organizational hesitation stemming from resistance to change, lack of top management support, and inadequate infrastructure, which impede AI adaptation. The article provides practical recommendations, including the development of ethical guidelines, robust security measures, regulatory preparedness, and employee training programs, to help organizations navigate these challenges and achieve secure, ethical, and effective AI integration.

While artificial intelligence (AI) offers transformative benefits for businesses, the road to successful adoption is often filled with technical, financial, and cultural hurdles. This chapter explores the most common barriers to AI integration and provides strategic solutions to overcome them, ensuring that businesses—especially small enterprises—can effectively harness AI’s potential. The chapter first examines technical difficulties, including data management issues, AI system integration challenges, and a lack of in-house expertise. It outlines practical solutions, such as data enrichment, phased AI implementation, and workforce upskilling, to help businesses navigate these complexities. Financial constraints, such as high initial costs, uncertain return on investment, and ongoing maintenance expenses, are also analyzed. The chapter provides cost-effective AI adoption strategies, including scalable AI solutions, leveraging cloud-based AI services, and securing grants or partnerships to mitigate financial risks. Additionally, the chapter delves into cultural and organizational resistance, addressing employee concerns, leadership alignment, and the need for AI literacy. Strategies for fostering an AI-ready culture include transparent communication, structured training programs, and leadership-driven AI advocacy. Lastly, ethical considerations—such as AI bias, transparency, data privacy, and regulatory compliance—are discussed to ensure responsible AI adoption. By the end of this chapter, business owners and entrepreneurs will be equipped with actionable insights to overcome AI adoption challenges and drive ethical, efficient, and sustainable AI integration in their organizations.

The accelerating integration of Artificial Intelligence (AI) across the economic, social, and institutional domains of nations worldwide marks one of the most consequential technological shifts of the twenty-first century. This study critically examines the development trajectory of AI from its conceptual origins to its contemporary multi-sectoral applications, analysing both the transformative benefits it offers and the complex challenges that accompany its adoption. Drawing on comparative evidence from developed and developing economies—with particular focus on India, Kenya, Brazil, Rwanda, and Vietnam—the paper charts the divergent paths nations have taken in harnessing AI for growth and social equity. The research further evaluates the evolving global regulatory landscape, interrogating how legislative frameworks in the United States, European Union, and emerging markets attempt to govern AI deployment responsibly. Findings reveal that while AI holds significant promise for inclusive development, bridging the adoption gap requires coordinated investment in infrastructure, human capital, and governance. The study concludes by articulating a framework for ethical and inclusive AI integration that can inform policy across diverse national contexts. Keywords: Artificial Intelligence, Machine Learning, Global Development, International Policy, Ethics, Inclusive Growth, Regulatory Frameworks.

Artificial Intelligence (AI) is increasingly pivotal in addressing global challenges by integrating technological advancements into environmental conservation and societal development. This paper explores the dual nature of AI's role—its capacity to foster sustainability and societal improvement, alongside the challenges it introduces. The research highlights AI applications in environmental management, such as optimizing resource use, monitoring ecosystems, and enhancing renewable energy efficiency. In societal contexts, AI's transformative potential extends to healthcare, education, and urban planning, fostering equitable access to essential services. However, significant barriers, including data scarcity, limited technical infrastructure, regulatory gaps, and socio-economic inequalities, hinder widespread AI adoption. By analyzing secondary data from recent literature, this study identifies critical challenges and presents actionable policy recommendations to ensure inclusive, ethical, and impactful AI integration. The findings underscore the urgency of collaborative efforts among governments, industries, and academia to leverage AI for achieving the United Nations Sustainable Development Goals (SDGs) and addressing pressing global issues.

In the pursuit of fostering sustainable digital banking (SDB), this study investigates the nexus between end‐users' digital competencies, perceptions of artificially intelligent systems, and sustainable practices. With a focus on addressing inclusivity barriers and promoting ethical considerations, expert interviews were employed to provide nuanced insights. The study's foundation rests on the significance of sustainable development goals, digital competencies, and artificial intelligence (AI) ethics in shaping the future of digital banking. An interview schedule was developed and distributed online to IT experts and researchers in the field of AI. The instrument examined the major challenges faced by customers in accessing digital banking, the expected timeline for the complete digitalization of the South African banking sector, customers' anticipated competency, and comfort levels with a 100% digital banking interface, and their perspectives on the overall impact of such a transition on humanity. The interview gathered participants' views on AI‐based banking systems that determine credit scores and creditworthiness. The study also included questions to obtain expert opinions on customers' digital capabilities, access to resources, and the impact of AI trust issues on financial service customers. The findings of this study provided valuable insights for stakeholders, including governments, NGOs, and financial institutions. Implications derived from expert perspectives illuminate strategies for enhancing digital competencies, addressing inclusivity hurdles, and ensuring ethical AI integration. This study contributes to the sustainable development discourse by offering actionable recommendations for stakeholders in the pursuit of a more inclusive, digitally competent, and ethically responsible digital banking landscape.

This research aims to critically examine the ethical integration of artificial intelligence (AI) in education through the perspective of maqāṣid al-sharīʿah, emphasizing the alignment between technological innovation and Islamic moral principles. The methods used are a systematic literature review and thematic content analysis against peer-reviewed publications for the period 2015–2025, which discuss the application of AI in primary, secondary, and higher education. The study identified dominant ethical issues, such as data privacy, algorithmic bias, accountability, human agency, and moral development, which were then mapped to Islamic ethical goals, including ʿadl (justice), amānah (belief), karāmah al-insān (human dignity), and ḥifẓ al-ʿaql (protection of reason). The results of the analysis show that the adoption of AI in education often emphasizes efficiency, personalization, and predictive analytics, but has the potential to reduce learners' autonomy and ethical reasoning. The mapping of maqāṣid al-sharīʿah shows a strong normative conformity, so that Islamic principles can be a moral foundation as well as a practical guide for AI governance. The research contribution is theoretical by bridging the literature on AI ethics and Islamic educational philosophy, as well as practical by offering an integrative framework for AI policymakers, educators, and developers. The integration of maqāṣid al-sharīʿah in AI governance ensures justice, trust, inclusivity, and the development of the whole human being (insān kāmil).

BACKGROUND: Artificial intelligence (AI) is increasingly used in healthcare research for data analysis and prediction. However, its rapid adoption raises ethical concerns, particularly in nursing, related to patient-centered care, fairness, and trust. AIM: This evidence-based practice (EBP) project systematically reviewed ethical guidelines concerning AI use in nursing research and developed recommendations specifically tailored to the Saudi context. METHODOLOGY: The Johns Hopkins Nursing EBP model informed the formulation of a Population, Intervention, Comparison, and Outcome question and guided the literature search, conducted between January 2025 and April 2025. PubMed, CINAHL, and EBSCOhost databases were searched for English-language, peer-reviewed studies published between 2020 and 2025 that addressed the ethical considerations of AI in healthcare research. Twelve articles fulfilled the inclusion criteria, encompassing systematic and scoping reviews, scholarly analyses, a comparative study, and policy or guideline documents. FINDINGS: Twelve studies revealed five recurring ethical domains in AI-related nursing research: accountability, transparency, fairness, data privacy, and human oversight. While global frameworks emphasize these principles, most evidence is based on expert consensus rather than empirical data. In Saudi Arabia, despite strong national AI governance through Saudi data and Artificial Intelligence Authority (SDAIA), nursing-specific ethical guidance and implementation remain limited, highlighting the need for localized frameworks and nurse-led research. RECOMMENDATIONS: Nursing researchers should integrate robust data protection, transparency, and informed consent processes into all AI-enabled studies to ensure the integrity of research. Institutions should align nursing research governance with SDAIA and Vision 2030 ethical standards while developing nursing-specific frameworks and training on AI ethics. Ongoing capacity-building, mentorship, and nurse-led empirical research are essential to operationalize these principles and ensure the responsible and culturally relevant adoption of AI in Saudi nursing research. CONCLUSION: Current ethical guidance on AI in nursing research relies primarily on expert consensus and international frameworks, with minimal empirical validation. Locally relevant guidelines that incorporate SDAIA regulations into nursing protocols are needed to support the ethical adoption of AI. Empirical studies led by nurses are crucial for operationalizing these principles.