Responsible Artificial Intelligence Research Articles

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.

AGENTIC GOVERNANCE: A FRAMEWORK FOR AUTONOMOUS DECISION-MAKING SYSTEMS

The proliferation of fake news has become a significant concern in recent years, with far-reaching consequences for individuals, communities, and society. Artificial intelligence (AI) has the potential to play a crucial role in detecting and mitigating the spread of fake news. However, the use of AI in fake news detection also raises important governance considerations. In this paper, we propose a novel approach to AI governance in fake news detection, including a framework for responsible AI governance, a new algorithm for fake news detection, and a comprehensive evaluation of the proposed approach.

Does the Media’s Partisanship Influence News Coverage on Artificial Intelligence Issues? Media Coverage Analysis on Artificial Intelligence Issues

This study aims to analyze news coverage on artificial intelligence (AI) issues and highlight the characteristics and differences in reporting based on media partisanship. By examining AI-related news in the South Korean media, this study reveals how conservative and progressive outlets frame the issue differently. The analysis found that conservative media coverage predominantly focuses on positive aspects, emphasizing development value frames such as the benefits and societal progress brought by AI. In contrast, progressive media often highlight crisis value frames, focusing on issues like side effects, ethical concerns, and legislation surrounding AI. These partisan differences reflect fundamental societal priorities and influence public discourse and policy agendas. Understanding media framing is crucial for fostering informed public dialogue on the societal significance of AI and promoting evidence-based decision-making. By recognizing partisan biases and critically evaluating media coverage, citizens can engage in constructive discourse beyond ideological divides. This study underscores the role of the media in promoting interdisciplinary discussions about the future trajectory of AI and in preparing society for its impacts. Ultimately, evidence-based public discourse is essential for shaping responsible AI policies and mitigating potential risks in the digital age.

The transformative impact of large language models on medical writing and publishing: current applications, challenges and future directions.

Large language models (LLMs) are rapidly transforming medical writing and publishing. This review article focuses on experimental evidence to provide a comprehensive overview of the current applications, challenges, and future implications of LLMs in various stages of academic research and publishing process. Global surveys reveal a high prevalence of LLM usage in scientific writing, with both potential benefits and challenges associated with its adoption. LLMs have been successfully applied in literature search, research design, writing assistance, quality assessment, citation generation, and data analysis. LLMs have also been used in peer review and publication processes, including manuscript screening, generating review comments, and identifying potential biases. To ensure the integrity and quality of scholarly work in the era of LLM-assisted research, responsible artificial intelligence (AI) use is crucial. Researchers should prioritize verifying the accuracy and reliability of AI-generated content, maintain transparency in the use of LLMs, and develop collaborative human-AI workflows. Reviewers should focus on higher-order reviewing skills and be aware of the potential use of LLMs in manuscripts. Editorial offices should develop clear policies and guidelines on AI use and foster open dialogue within the academic community. Future directions include addressing the limitations and biases of current LLMs, exploring innovative applications, and continuously updating policies and practices in response to technological advancements. Collaborative efforts among stakeholders are necessary to harness the transformative potential of LLMs while maintaining the integrity of medical writing and publishing.

The synergy of artificial intelligence and nanotechnology towards advancing innovation and sustainability - A mini-review

The convergence of Artificial Intelligence (AI) and nanotechnology is a transformative frontier, holding vast potential for scientific and technological advancements. This review explores the integration of AI and nanotechnology, aiming to uncover current trends, challenges, and future directions across various domains. A scientometric approach was employed, gathering and analyzing existing literature and research in the fields of AI and nanotechnology. Through a systematic examination of recent studies and developments, we identified key trends, challenges, and potential pathways for future research and applications. Findings from this review highlight the significant impact of AI-driven nanotechnology in materials discovery and design, healthcare, environmental monitoring, energy conversion, and quantum computing. These areas have witnessed remarkable advancements, such as the development of novel nanomaterials, precise drug delivery systems, advanced nanosensors, and efficient energy storage solutions. However, challenges persist, including insufficient interdisciplinary collaboration, data quality, ethical concerns, and regulatory frameworks. While challenges require careful consideration and mitigation, the potential for innovation and progress in diverse domains is undeniable. This review underscores the importance of responsible AI use and ongoing research to unlock the full potential of this convergence, ultimately shaping the future of science and technology.

Artificial intelligence ethics: ethical consideration and regulations from theory to practice

<p><span lang="EN-US">The advancement of artificial intelligence (AI) has led to its widespread use in sectors such as finance, healthcare, military, and employment in developed countries. However, this reliance has raised concerns about AI governance, particularly regarding algorithmic biases based on skin color, gender, race, and age. Consequently, many countries have introduced regulations and ethical frameworks to address these issues. The Ministry of Digital Economy and Entrepreneurship in Jordan has included AI in its 2022 plan, signaling significant progress. The integration of AI in education programs underscores this commitment. However, addressing AI's potential negative impacts is essential. We propose ethical considerations and regulations for AI to complement Jordan's initiatives. Our research aims to promote responsible AI usage by developing ethical guidelines in Jordan. It presents techniques to identify and mitigate biases related to skin color, gender, and age in AI outputs and datasets. The research includes extensive testing on datasets, analyzing approximately 100 images, and revealing notable error rates, including a 16% error rate in detecting skin color, a 4% error rate in seeing white faces, and a 6% error rate in identifying females over men. Therefore, ethical considerations and regulations for AI applications in Jordan must be implemented.</span></p>

Unlocking the potential: Responsibly embracing artificial intelligence to advance the use of health data and analytics at the Canadian Institute for Health Information.

Canadian Institute for Health Information (CIHI) is looking to modernize and adopt new ways of working. This incudes the use of new technology, including the application of Artificial Intelligence (AI). To begin in a purposeful manner, the organization developed an AI strategy which was informed through feedback from key stakeholders and partners, from its staff and from a review of international research. The research informed several ways AI could add value to CIHI's internal operations and to the external role CIHI could play in advancing responsible AI adoption in health systems across Canada. This article describes the strategy development process and the areas of focus within the strategy.

Analysis of the use of artificial intelligence in the digital financial environment in Ukraine

The integration of artificial intelligence (AI) in the digital financial environment of Ukraine presents numerous opportunities and challenges. The purpose of the paper is to analyze the current state and future prospects of AI applications in the Ukrainian financial sector. By examining successful case studies, the study will evaluate the effectiveness of AI implementations in banks and financial institutions. Key success factors and barriers to AI adoption will be identified, providing insights into best practices and common pitfalls. The regulatory landscape is crucial for fostering innovation while ensuring security and compliance. This research will explore the existing regulatory framework in Ukraine, identifying gaps and proposing recommendations to enhance the legislative support for AI advancements in the financial sector. Understanding how regulations can balance innovation with consumer protection is essential for sustainable growth. Data protection and cybersecurity are critical issues in the digital financial environment. The study will investigate the impact of AI on these areas, assessing how AI technologies can both bolster and threaten security measures. By analyzing current strategies and technologies used to protect financial data, the research will offer insights into improving cybersecurity in AI-driven financial services. Additionally, the social and economic implications of AI adoption will be explored. The research will examine how AI affects employment in the financial sector, considering both the potential for job displacement and the creation of new roles. The changing skill requirements and the need for workforce retraining will be discussed, emphasizing the importance of education and training programs to prepare for an AI-driven economy. Furthermore, the study will delve into the ethical considerations surrounding AI use in finance. Issues such as algorithmic bias, transparency, and accountability will be analyzed to ensure that AI applications are fair and equitable. By addressing these ethical concerns, the research aims to contribute to the development of responsible AI practices in the financial industry. In conclusion, this research will provide a comprehensive analysis of AI in the digital financial environment of Ukraine. By addressing the technological, regulatory, security, social, and ethical dimensions, the study aims to offer a holistic view of AI’s impact and potential in the Ukrainian financial sector. The findings will be valuable for policymakers, financial institutions, and technology developers, guiding them towards effective and responsible AI integration.

Examining the applicability of the Protection of Personal Information Act in AI-driven environments

Background: Technological advancements have heightened the importance of safeguarding individual privacy and data. In response to these challenges, South Africa introduced the Protection of Personal Information (POPI) Act. This legislation established robust legal frameworks aimed at protecting confidential information and upholding individuals’ right to anonymity. However, there is a significant research gap regarding the POPI Act’s direct implications and effectiveness in the context of artificial intelligence (AI) adoption and utilisation. Understanding the interplay between the POPI Act and AI technologies is crucial for ensuring regulatory compliance, safeguarding personal data and fostering responsible AI deployment in South Africa.Objectives: This study investigates the POPI Act’s applicability in addressing privacy issues related to AI adoption in various sectors.Method: The research uses a document review methodology to analyse the documents and synthesise the results. This approach offers efficiency, accessibility, cost-effectiveness and non-intrusiveness benefits, making it a valuable tool for qualitative research across various disciplines.Results: Despite the POPI Act’s guiding principles aligning with key concepts of personal information protection, there are several gaps in its applicability to AI advancements across various sectors.Conclusion: The study emphasises the need for a dynamic legal framework that evolves with AI advancements, advocating for the incorporation of more stringent measures to address emerging privacy concerns.Contribution: The research contributes to the ongoing discourse on data protection and AI by highlighting the need for a forward-thinking legal framework that balances innovation and privacy, ensuring that the POPI Act remains effective in the face of evolving technologies.

On the interpretability of quantum neural networks

Interpretability of artificial intelligence (AI) methods, particularly deep neural networks, is of great interest. This heightened focus stems from the widespread use of AI-backed systems. These systems, often relying on intricate neural architectures, can exhibit behavior that is challenging to explain and comprehend. The interpretability of such models is a crucial component of building trusted systems. Many methods exist to approach this problem, but they do not apply straightforwardly to the quantum setting. Here, we explore the interpretability of quantum neural networks using local model-agnostic interpretability measures commonly utilized for classical neural networks. Following this analysis, we generalize a classical technique called LIME, introducing Q-LIME, which produces explanations of quantum neural networks. A feature of our explanations is the delineation of the region in which data samples have been given a random label, likely subjects of inherently random quantum measurements. We view this as a step toward understanding how to build responsible and accountable quantum AI models.

Exploring social and computer science students’ perceptions of AI integration in (foreign) language instruction

Artificial intelligence (AI) has gained acceptance in the field of education. Nevertheless, existing research on AI in education, particularly in foreign language (FL) learning and teaching, is notably limited in scope and depth. In the present study, we addressed this research gap by investigating social and computer science students' perceptions of the integration and use of AI-based technologies in education, focusing specifically on foreign language teaching. Using an online questionnaire, we analysed factors such as students' field of study, gender differences, and the type of AI used. The questionnaire included statements categorised into thematic clusters, with responses measured on a five-point Likert scale. Statistical analysis, including chi-square tests and Cohen's d, revealed that individuals studying computer science, males, and supporters of generative AI are more likely to use AI tools for educational purposes. They perceive fewer barriers to the integration of AI into FL education. Social science students and women are less likely to use AI tools in FL education and express scepticism about their potential to improve academic outcomes. They tend to be more critical or cautious regarding the role of AI in FL education. They view AI as a valuable tool that enhances the learning experience but, at the same time, recognise the irreplaceable role of human teachers. The study highlights the need for targeted educational initiatives to address gender and disciplinary gaps in AI adoption, promote informed discussions on AI in education, and develop balanced AI integration strategies to improve FL learning. These findings suggest educators and policymakers should implement comprehensive AI training programs and ethical guidelines for responsible AI use in (FL) education.

AI and the Future of Teaching: Preservice Teachers’ Reflections on the Use of Artificial Intelligence in Open and Distributed Learning

The rapid advancement of artificial intelligence (AI) in education underscores transformative prospects for open and distributed learning, encompassing distance, hybrid, and blended learning environments. This qualitative study, grounded in narrative inquiry, investigates the experiences and perceptions of 141 preservice teachers engaged with AI, mainly through ChatGPT, over a 3-week implementation on Zoom to understand its influence on their evolving professional identities and instructional methodologies. Employing Strauss and Corbin’s methodological approach of open, axial, and selective coding to analyze reflective narratives, the study unveils significant themes that underscore the dual nature of AI in education. Key findings reveal ChatGPT’s role in enhancing educational effectiveness and accessibility while raising ethical concerns regarding academic integrity and balanced usage. Specifically, ChatGPT was found to empower personalized learning and streamline procedures, yet challenges involving information accuracy and data security remained. The study significantly contributes to teacher education discourse by revealing AI’s complex educational impacts, highlighting an urgent need for comprehensive ethical AI literacy in teacher training curricula. However, critical ethical considerations and practical challenges involving academic integrity, information accuracy, and balanced AI use are also brought to light. The research also spotlights the need for responsible AI implementation in open and distributed learning to optimize educational outcomes while addressing potential risks. The study’s insights advocate for future-focused AI literacy frameworks that integrate technological adeptness with ethical considerations, preparing teacher candidates for an intelligent digital educational landscape.

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

BackgroundWith the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption. ObjectivesThis study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings. MethodsWe employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail. ResultsSix themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare. ConclusionsThe findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.

Decoding India’s AI Governance Strategy and its Implications for the U.S.-India Bilateral Relationship

India is developing a unique approach to the governance of artificial intelligence (AI) and is staking out a leadership role in multilateral dialogues on responsible AI. This paper reviews India’s available AI governance choices, as the country defines its regulatory model, and highlights their associated trade-offs. It also analyses these choices in light of the government’s national priorities, as well as India’s perceived advantages and weaknesses in various industries that might utilize AI systems. With this background, this paper examines how India’s current approach to AI governance may impact the trajectory of U.S.-India technology cooperation.

Implications of causality in artificial intelligence.

Over the last decade, investment in artificial intelligence (AI) has grown significantly, driven by technology companies and the demand for PhDs in AI. However, new challenges have emerged, such as the 'black box' and bias in AI models. Several approaches have been developed to reduce these problems. Responsible AI focuses on the ethical development of AI systems, considering social impact. Fair AI seeks to identify and correct algorithm biases, promoting equitable decisions. Explainable AI aims to create transparent models that allow users to interpret results. Finally, Causal AI emphasizes identifying cause-and-effect relationships and plays a crucial role in creating more robust and reliable systems, thereby promoting fairness and transparency in AI development. Responsible, Fair, and Explainable AI has several weaknesses. However, Causal AI is the approach with the slightest criticism, offering reassurance about the ethical development of AI.

Examining the Social Consequences of Automation and Artificial Intelligence in Industrial Management

This article examines the social consequences of automation and artificial intelligence (AI) in industrial management, drawing on the findings of Damioli, Van Rooy, and Vertsy’s (2021) study on the impact of AI on labor productivity. The integration of AI technologies in industrial settings has led to significant improvements in efficiency and productivity. However, these advancements come with substantial social implications that affect both the workforce and broader societal structures. Internally, automation and AI have shifted the demand from low-skilled to high-skilled labor, necessitating ongoing education and skill development for employees. Additionally, the reliance on AI for managerial decisions raises ethical concerns regarding algorithmic biases and the potential erosion of human judgment.Externally, the broader societal impact includes economic disparities, as regions with a high concentration of low-skilled jobs experience greater unemployment and underemployment. The displacement of workers due to automation presents challenges such as increased socio-economic divides and disruptions to community stability. Ethical considerations, including data privacy and security, are crucial to ensuring fair and responsible AI deployment. This article underscores the need for strategic collaboration between industry leaders, policymakers, and educational institutions to address these social consequences. By fostering an inclusive approach to technological advancement, it is possible to balance the productivity benefits of AI with the imperative to mitigate its adverse social impacts.

Responsible AI in Organizational Training: Applications, Implications, and Recommendations for Future Development

Through a literature review, this study investigates the responsibility, application, and impact of Artificial Intelligence (AI) in organizational training based on the theoretical frameworks of Psychological, Economic, and Systems Theories in Human Resource Development (HRD). It emphasizes the importance of responsible AI training systems that adhere to non-discrimination, privacy, interpretability, professional responsibility, and accountability to ensure AI’s beneficial and equitable contribution to training. The application scenarios of AI in areas such as knowledge management, training needs analysis, training delivery, and feedback to provide personalized and efficient training solutions are analyzed. Moreover, it highlights the differing impacts of AI-supported training on organizations, trainers, and trainees and the significance of stakeholder engagement. Finally, it proposes recommendations for future research to broaden our understanding of AI’s application in training and assess its effects on policies and practices, guiding organizations to adopt AI technologies per HRD principles and ethical standards.

Use of Artificial Intelligence in Medical Devices for Post-Market Surveillance

Artificial intelligence (AI) has emerged as a transformative tool in post-market surveillance (PMS) for monitoring the safety and performance of medical products. This article explores the role of AI in optimizing PMS practices, focusing on its applications in signal detection, risk assessment, and regulatory compliance. By harnessing machine learning algorithms and big data analytics, AI facilitates the automated analysis of real-world evidence, including patient outcomes data, adverse event reports, and electronic health records. Through pattern recognition and anomaly detection, AI algorithms enable the early identification of potential safety issues and facilitate timely interventions to mitigate risks. Moreover, AI-driven PMS systems enhance regulatory oversight by providing regulators with comprehensive and actionable insights into product safety profiles and emerging trends. However, concerns regarding data privacy, algorithm bias, and interpretability underscore the need for transparent and ethically responsible AI deployment in PMS frameworks.

Information Professionals’ Metaphorical Perceptions of Artificial Intelligence Concept

This study examines the metaphors used by 66 information professionals in Türkiye to conceptualize artificial intelligence (AI). Through qualitative metaphor analysis, rich insights emerged on perceptions of AI’s nature, functioning, relationships with humans, roles, and societal impacts. Some of the metaphors used are child, human, artist, human intelligence, robot, assistant, doctor, weapon, terminator, something scary, closed box, black hole, Pandora’s box, etc. Multiple, often contradictory metaphors like “child” and “uncontrollable power” indicate AI is viewed in a multidimensional way, evoking both promise and concern. Key findings suggest information professionals appreciate AI’s transformative potential in enhancing services but also harbor anxieties related to human relevance, technocracy, and social issues like privacy. Understanding these complex cognitive and emotional orientations can facilitate responsible AI adoption to augment information services. Gesturing at AI’s multifaceted connotations also foregrounds communication challenges complicating public debate and policymaking on emerging technologies. The value of this study lies in surfacing the nuanced perspectives of a stakeholder group positioned to mediate public understanding and direct integration of AI in information services. Mapping information professionals’ rich construals of AI also highlights the need for public communication attuned to metaphorical reasoning around new technologies.

ARTIFICIAL INTELLIGENCE – POWERED VIDEO CONTENT GENERATION TOOLS

This article discusses the considerations of artificial intelligence-powered video content generation tools, exploring their applications, ethical considerations, and evaluation criteria. Through discussions of various artificial intelligence (AI) tools, including features, limitations, and implications, the authors analyze the evolving landscape of video creation in the digital age. Key themes include the ethical implications of deep fake technology and the importance of responsible AI principles, exemplified by Microsoft's guidelines. This paper identifies five of the most promoted free social media tools. Evaluation criteria for these tools, such as visual quality, relevance, coherence, authenticity, and transparency, are examined to assess the suitability of AI-generated videos. While AI offers promising opportunities, the discussion underscores the continued need for human oversight and ethical considerations to ensure the responsible use of AI technologies in video content generation.