AI Ethics Research Articles

Intelligent Tutoring Systems, Generative Artificial Intelligence (AI), and Healthcare Agents: A Proof of Concept and Dual-Layer Approach.

This study introduces a novel methodology for enhancing intelligent tutoring systems (ITS)through the integration of generative artificial intelligence (GenAI) and specialized AIagents. We present a proof of concept (PoC) demo that implements a dual-layer GenAI validation approach that utilizes multiple large language models to ensure the reliability and pedagogical integrity of the AI-generated content. The system features role-specific AI agents, a GenAI-powered scoring mechanism, and an AI mentor that provides periodic guidance. This approach demonstrates capabilities in dynamic scenario generation and real-time adaptability while addressing key challenges in AI-driven education, such as personalization, scalability, and domain-specific knowledge integration. Although exemplified here through a case study in healthcare root cause analysis, the methodology is designed for broad applicability across various fields. Our findings suggest that this approach has significant potential for advancing adaptive learning and personalized instruction while raising important considerations regarding ethical AI application in education. This work provides a foundation for further research into the efficacy and impact of GenAI-enhanced ITS on learning outcomes and instructional design across diverse educational domains.

Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy

There is growing recognition among researchers and stakeholders about the significant impact of artificial intelligence (AI) technology on classroom instruction. As a crucial element in developing AI literacy, AI education in K-12 schools is increasingly gaining attention. However, most existing research on K-12 AI education relies on experiential methodologies and suffers from a lack of quantitative analysis based on extensive classroom data, hindering a comprehensive depiction of AI education's current state at these educational levels. To address this gap, this article employs the advanced semantic understanding capabilities of large language models (LLMs) to create an intelligent analysis framework that identifies learning theories, pedagogical approaches, learning tools, and levels of AI literacy in AI classroom instruction. Compared with the results of manual analysis, analysis based on LLMs can achieve more than 90% consistency. Our findings, based on the analysis of 98 classroom instruction videos in central Chinese cities, reveal that current AI classroom instruction insufficiently foster AI literacy, with only 35.71% addressing higher-level skills such as evaluating and creating AI. AI ethics are even less commonly addressed, featured in just 5.1% of classroom instruction. We classified AI classroom instruction into three categories: conceptual (50%), heuristic (18.37%), and experimental (31.63%). Correlation analysis suggests a significant relationship between the adoption of pedagogical approaches and the development of advanced AI literacy. Specifically, integrating Project-based/Problem-based learning (PBL) with Collaborative learning appears effective in cultivating the capacity to evaluate and create AI.

The Ethical Responsibilities of Researchers in Light of the Technological Advancement and Artificial Intelligence Methods: A Case Study of Management Ph.D. Researchers at Midocean University

This study aimed to assess the integration, ethical considerations, and governance of artificial intelligence (AI) within the PhD programs at Midocean University. It specifically sought to understand PhD researchers' perceptions and attitudes towards AI and identify areas for enhancement in AI-related policies and educational initiatives. A descriptive analytical approach was adopted, utilizing an electronic questionnaire distributed to 105 PhD researchers, with 54 completing the survey. The questionnaire was designed to measure various aspects of AI usage, ethical concerns, and governance practices. Statistical analysis was conducted to evaluate the relationships between AI awareness, ethical application, and governance perceptions. The findings revealed diverse perceptions of AI among researchers, indicating both opportunities and challenges in AI integration. The statistical analysis confirmed significant correlations between AI awareness and positive perceptions of ethical AI usage. However, the response rate and sample size posed limitations on the generalizability of the results. The study highlights the need for Midocean University to strengthen its AI governance frameworks and expand ethical guidelines to keep pace with technological advancements. Recommendations include enhancing AI-related educational programs, updating AI policies regularly, and promoting interdisciplinary research to foster an informed and ethically aware research community.

Ethical AI in Healthcare: Balancing Innovation with Privacy and Compliance

Within the dynamic and ever-changing healthcare industry, Artificial Intelligence (AI) emerges as a powerful and revolutionary concept capable of transforming patient care, optimising operational processes, and improving clinical decision-making. Nevertheless, the incorporation of artificial intelligence (AI) into healthcare systems gives rise to substantial ethical considerations, especially pertaining to privacy and adherence to regulations. This work examines the ethical aspects of artificial intelligence (AI) in the healthcare sector, highlighting the importance of maintaining a harmonious equilibrium between technical advancement and strict compliance with privacy regulations and legal obligations. Moreover, the use of artificial intelligence in healthcare must adhere to legislative frameworks specifically created to safeguard the rights of humans. This encompasses not just compliance with current rules but also the proactive identification and resolution of developing legal norms as AI technology advances. Healthcare practitioners and artificial intelligence (AI) developers should cooperate to create explicit standards and procedures that conform to existing legislation and expected future rules.

Transformative Impact of the EU AI Act on Maritime Autonomous Surface Ships

The International Maritime Organization collaborates with member states to develop the Maritime Autonomous Surface Ships (MASS) Code to establish an international agreement framework. In December 2023, an agreement was reached on the European Union (EU) Artificial Intelligence (AI) Act within the MASS Code. It was officially finalized and implemented on 1 August 2024. While the Act’s full application is set for two years later, certain provisions regarding safety against AI risks will take effect sooner. This study utilized a multiple-streams analysis method to examine how the EU AI Act impacted shipyards and shipping companies, focusing on ethical AI use and user safety. The findings indicate that technical completeness, safety enhancement, and AI system regulation through risk classification influence autonomous ship innovation. The challenges of reviewing the commercialization of new technologies for autonomous ships may result in market failures or decreased industry competitiveness. The study emphasizes the need to balance AI safety and ethical responsibility with autonomous ship development innovations. To ensure the sustainable application of AI technology, innovators must adapt to the EU AI Directive. Hence, this study confirms that the ethical and legal regulation of AI technology is crucial in technological innovation and development related to autonomous ships.

Unveiling the Black Box: A Comprehensive Review of Explainable AI Techniques

As artificial intelligence (AI) continues to integrate into various sectors, the complexity and opacity of AI models, particularly in machine learning (ML), pose significant challenges to interpret-ability and trust. This review paper addresses the critical need for explainable AI (XAI) to enhance understanding and transparency in ML models. We provide a comprehensive survey of state-of-the-art XAI techniques, including feature importance methods such as LIME (Local Interpret- able Model-agnostic Explanations) and SHAP (Shapely Additive expla- nation), as well as perturbation and attention-based mechanisms, to elucidate model decisions. Our analysis spans a diverse range of applications, including finance, education, and healthcare, showcasing the practical utility and impact of XAI methods. We discuss crucial issues such as the trade-offs between model accuracy and interpret ability, the de- sign of user-friendly explanations, and the development of comprehensive evaluation metrics. Furthermore, we explore the implications of XAI on user trust and decision-making, emphasizing the importance of reliable and ethical AI systems. This review contributes to the ongoing efforts to make AI systems more interpret- able, reliable, and aligned with societal needs, providing a robust foundation for future research and practical implementations of XAI. Keywords: Explainable AI · Machine Learning · Interpret-ability · Transparency · Ethical AI · XAI Techniques.

Preservice ethics teachers’ perceptions of AI ethics education

ABSTRACT In the ever-evolving landscape of AI, moral educators play a crucial role in promoting the positive development of youth and addressing potential ethical challenges posed by AI. This study employed Q methodology to explore the perceptions of 30 South Korean preservice ethics teachers on AI ethics education, and five distinct perception types emerged: Human-Centered Ethical Guardians of AI, AI Skeptics, AI Abuse Preventers, Competence Cultivators for Coexistence with AI, and AI Social Justice Advocates. These five types encapsulate the distinct gestalt of individuals that intertwines various perceptions regarding AI ethics principles as well as AI technology, the future of the AI era, and the purpose of AI ethics education. By aligning educational approaches with individuals’ perception types, teacher education programs can contribute to a more balanced viewpoint on AI and AI ethics education among preservice teachers.

The future of artificial intelligence: Time to embrace more international collaboration

In an era of rapid Artificial Intelligence (AI) growth, there is both excitement and concern about its impact. While AI poses ethical challenges, it also offers unprecedented opportunities to address global issues like climate change, health crises, and urbanization. Given AI's dual nature, international collaboration is essential, particularly between leading nations like the U.S. and China. We propose creating a specialized international organization to develop and enforce ethical AI guidelines. This organization would facilitate global cooperation, data sharing, and joint R&D, ensuring that AI advancements serve the collective benefit of humanity.

Tools for Enhancing Research Outputs through AI Content Creation: An Analytical Review

In the fast-paced realm of modern research, the incorporation of Artificial Intelligence (AI) technologies has fundamentally transformed scholarly practices of creating and disseminating knowledge. By harnessing AI tools and algorithms, researchers can optimize writing workflows, improve data analysis, maintain linguistic accuracy, and keep up with evolving trends in their respective domains. Through an extensive examination of literature and case studies, this study illuminates AI’s capacity to enhance the influence of research publications. Moreover, it delves into the challenges associated with AI utilization, stressing the importance of ethical considerations and responsible AI adoption within academic circles.

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

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

Transforming Data Analytics with AI for Informed Decision-Making

This study delves into how advanced data analytics and artificial intelligence (AI) can work together to enhance decision-making processes. As we navigate today’s data-driven environment, discovering the synergy between these fields is crucial, given the growing complexity of datasets. Advanced analytical tools are essential, and AI offers exceptional capabilities in pattern recognition and automation. This research investigates how cosmbining data analytics techniques—such as Predictive Modeling, Clustering, and Trend Analysis—with AI approaches like Machine Learning and Deep Learning can improve decision-making. A key focus of the study is on making AI models more interpretable and transparent. It emphasizes the importance of ensuring that AI-driven decisions are clear and understandable. Additionally, the research addresses ethical considerations and the need for human-centered design, aiming to balance AI’s power with openness. It also strives for responsible AI use by tackling issues such as bias and promoting ethical practices in the application of advanced data analytics and AI. The study demonstrates practical applications in areas like healthcare and finance, showing how these technologies can transform personalized medicine, disease prediction, risk assessment, fraud detection, and market trend analysis. Overall, this research highlights the valuable interaction between advanced data analytics and AI, offering a guide for organizations to enhance their decision-making while adhering to ethical standards and responsible AI use.

Challenges and Limitations of Human Oversight in Ethical Artificial Intelligence Implementation in Health Care: Balancing Digital Literacy and Professional Strain

Challenges and Limitations of Human Oversight in Ethical Artificial Intelligence Implementation in Health Care: Balancing Digital Literacy and Professional Strain

Rethinking AI: bias in speech-recognition chatbots for ELT

Abstract Artificial intelligence (AI) technologies, particularly chatbots with speech-recognition, are gaining attention as tools for ELT. However, this frontline development in contemporary ELT seems to stand in stark contrast to the multilingual effort, another innovative trend, as chatbots’ speech recognition capabilities are primarily attuned to native English speaker pronunciation. Addressing this concern, this study adopted a Global Englishes (GE) perspective and examined students’ attitudes toward English after their use of a speech-recognition chatbot. Text-mining analysis of reflection papers from 38 students and thematic analysis of interviews with seven students revealed that the chatbot hindered the incorporation of diverse English use into ELT and students expressed a conception of English not as a language in flux but rather as a subject with specific content to be mastered. The findings are discussed concerning GE, AI ethics, and computer-assisted language learning, and implications for future chatbot use in ELT are provided.

The rise of checkbox AI ethics: a review

AbstractThe rapid advancement of artificial intelligence (AI) sparked the development of principles and guidelines for ethical AI by a broad set of actors. Given the high-level nature of these principles, stakeholders seek practical guidance for their implementation in the development, deployment and use of AI, fueling the growth of practical approaches for ethical AI. This paper reviews, synthesizes and assesses current practical approaches for AI in health, examining their scope and potential to aid organizations in adopting ethical standards. We performed a scoping review of existing reviews in accordance with the PRISMA extension for scoping reviews (PRISMA-ScR), systematically searching databases and the web between February and May 2023. A total of 4284 documents were identified, of which 17 were included in the final analysis. Content analysis was performed on the final sample. We identified a highly heterogeneous ecosystem of approaches and a diverse use of terminology, a higher prevalence of approaches for certain stages of the AI lifecycle, reflecting the dominance of specific stakeholder groups in their development, and several barriers to the adoption of approaches. These findings underscore the necessity of a nuanced understanding of the implementation context for these approaches and that no one-size-fits-all approach exists for ethical AI. While common terminology is needed, this should not come at the cost of pluralism in available approaches. As governments signal interest in and develop practical approaches, significant effort remains to guarantee their validity, reliability, and efficacy as tools for governance across the AI lifecycle.

Youth perceptions of AI ethics: a Q methodology approach

ABSTRACT AI technology advancement has sparked a global initiative to educate youth on AI ethics. Understanding students’ prior knowledge is vital. This study explores the diverse perceptions of AI ethics among Korean middle school students using Q methodology. Four types emerged: Privacy Guardians, AI Coexistence Pursuers, AI Ethics Conservatives, and Domestic Distributive Justice Advocates. These classifications reflect the students’ concerns, attitudes toward AI, and value preferences. Despite differences, there is consensus on the importance of human dignity and disagreement with the fair distribution of economic benefits of AI between countries. The findings suggest the need for tailored educational strategies based on students’ perceptions to foster balanced ethical reflection.

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

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

Managing workplace AI risks and the future of work.

Artificial intelligence (AI)-the field of computer science that designs machines to perform tasks that typically require human intelligence-has seen rapid advances in the development of foundation systems such as large language models. In the workplace, the adoption of AI technologies can result in a broad range of hazards and risks to workers, as illustrated by the recent growth in industrial robotics and algorithmic management. Sources of risk from deployment of AI technologies across society and in the workplace have led to numerous government and private sector guidelines that propose principles governing the design and use of trustworthy and ethical AI. As AI capabilities become integrated in devices, machines, and systems across industry sectors, employers, workers, and occupational safety and health practitioners will be challenged to manage AI risks to worker health, safety, and well-being. Five risk management options are presented as ways to assure that only trustworthy and ethical AI enables workplace devices, machinery, and processes. AI technologies will play a significant role in the future of work. The occupational safety and health practice and research communities need to ensure that the promise of these new AI technologies results in benefit, not harm, to workers.

LEGAL ANALYSIS OF EU ARTIFICIAL INTELLIGENCE ACT (2024): INSIGHTS FROM PERSONAL DATA GOVERNANCE AND HEALTH POLICY

Background: This study correlates the up-to-date ethical, functional and legal evaluations related to the management and governance of artificial intelligence (AI) under European Union (EU) law, particularly impacting the health data sector and medical standards as provided by the Artificial Intelligence Act within the Regulation adopted by the European Council in May 2024. The initial proposal for the management and governance of the AI sector was submitted in April 2021. Three years later, on 13 March 2024, the European Union Artificial Intelligence Act (EU AIA) was adopted by the European Parliament. Subsequently, on 21 May 2024, the Council adopted an innovative legislative framework that harmonises the standards and rules for AI regulation. This framework is set to take effect in May 2026, with the central objective of stimulating and motivating a fair, safe, legal single market that respects the principles of ethics and the fundamental rights of the human person. Methods: The current legal analysis focuses on the European Union’s new institutional governance involving a multistage approach to managing health data, ethical artificial intelligence, generative artificial intelligence and classification of types of AI by considering the degree of risk (e.g. artificial intelligence systems with limited risk and systems with high risk) and medical devices. It outlines the legal framework for AI regulation and governance in the EU by focusing on compliance with the previously adopted legislation in the Medical Devices Regulation (2017) and the In-Vitro Diagnostic Regulation (2017). The paper also examines the application of the newly adopted EU Artificial Intelligence Act in relation to national justice systems, previous EU regulations on medical devices and personal data protection regulation, and its correlation with the European Court of Human Rights jurisprudence. This opens up complex discussions related to judicial reform and access to justice. For this purpose, as a research objective, the legal analysis includes an innovative perspective following an integrative discussion on the latest legal reforms and regulations of the AI sector in Eastern Europe launched in 2024 with a special focus on the latest developments in the EU Candidate Countries namely Ukraine and the Republic of Moldova. Results and conclusions: The present research facilitates the exploration of the real benefits of managing innovative AI systems for medical data, research, and development, as well as within the medical technology industry.

Ensuring fundamental rights compliance and trustworthiness of law enforcement AI systems: the ALIGNER Fundamental Rights Impact Assessment

AbstractArtificial intelligence systems can expand the capabilities and enhance the efficiency of law enforcement agencies preventing, investigating, detecting, and prosecuting criminal offences in the European Union. At the same time, the deployment of artificial intelligence in the security domain often raises numerous legal and ethical concerns. The ALIGNER Fundamental Rights Impact Assessment is an operational tool, rooted in fundamental rights and in the principles of AI ethics, ready to be integrated in the AI governance measures of European law enforcement agencies to inform their decision-making processes and ensure compliance with the recently adopted Artificial Intelligence Act. This paper first introduces the main tensions between law enforcement AI and fundamental rights, as enshrined in the Charter of Fundamental Rights of the European Union; then, it gives an overview of the main developments and best practices in AI governance and their relationship with fundamental rights as well as AI ethics; and finally, it describes the structure of the ALIGNER Fundamental Rights Impact Assessment.

Capturing the unobservable in AI development: proposal to account for AI developer practices with ethnographic audit trails (EATs)

AbstractThe prevalence of artificial intelligence (AI) tools has inspired social studies researchers, ethicists, and policymakers to seriously examine AI’s sociopolitical and ethical impacts. AI ethics literature provides guidance on which ethical principles to implement via AI governance; AI auditing literature, especially ethics-based auditing (EBA), suggests methods to verify if such principles are respected in AI model development and deployment. As much as EBA methods are abundant, I argue that most currently take a top-down and post-hoc approach to AI model development: Existing EBA methods mostly assume a preset of high-level, abstract principles that can be applied universally across contexts; meanwhile, current EBA is only conducted after the development or deployment of AI models. Taken together, these methods do not sufficiently capture the very developmental practices surrounding the constitution of AI models on a day-to-day basis. What goes on in an AI development space and the very developers whose hands write codes, assemble datasets, and design model architectures remain unobserved and, therefore, uncontested. I attempt to address this lack of documentation on AI developers’ day-to-day practices by conducting an ethnographic “AI lab study” (termed by Florian Jaton), demonstrating just how much context and empirical data can be excavated to support a whole-picture evaluation of AI models’ sociopolitical and ethical impacts. I then propose a new method to be added to the arsenal of EBA: Ethnographic audit trails (EATs), which take a bottom-up and in-progress approach to AI model development, capturing the previously unobservable developer practices.