Ethical Governance Research Articles

A critical review towards artificial general intelligence: Challenges, ethical considerations, and the path forward

The pursuit of Artificial General Intelligence (AGI) has captivated researchers and industry leaders alike, promising a future where machines possess human-like cognitive abilities. However, this ambitious endeavor is fraught with multifaceted challenges and ethical dilemmas that necessitate careful examination. This critical review surveys the landscape of AGI research, identifying key hurdles and ethical considerations while outlining potential pathways forward. Firstly, technical challenges loom large on the path to AGI. These encompass fundamental problems such as developing robust learning algorithms capable of generalizing across diverse domains, as well as engineering systems that can exhibit adaptive and autonomous behavior akin to human intelligence. Additionally, ensuring the safety and reliability of AGI systems presents a formidable obstacle, with concerns ranging from algorithmic bias to the potential for catastrophic outcomes in unanticipated scenarios. Ethical considerations permeate every facet of AGI development and deployment. Questions of accountability, transparency, and control surface as central concerns, as the implications of relinquishing decision-making authority to autonomous systems raise profound ethical dilemmas. Moreover, the socio-economic ramifications of widespread AGI adoption, including job displacement and inequality, demand careful scrutiny and proactive mitigation strategies. Navigating these challenges requires a concerted effort from interdisciplinary stakeholders. Collaboration between computer scientists, ethicists, policymakers, and the public is essential to establish robust frameworks for the responsible development and deployment of AGI. Moreover, fostering an inclusive dialogue that prioritizes ethical principles and societal values is paramount in shaping a future where AGI augments human capabilities while safeguarding against potential risks. While the pursuit of AGI holds immense promise, its realization demands a holistic approach that addresses technical challenges alongside ethical considerations. By charting a path forward that prioritizes safety, transparency, and ethical governance, we can harness the transformative potential of AGI while ensuring its alignment with human values and interests.

Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability

As artificial intelligence (AI) continues to permeate various aspects of our lives, the ethical challenges associated with its development become increasingly apparent. This paper navigates and reviews the ethical dilemmas in AI development, focusing on strategies to promote transparency, fairness, and accountability. The rapid growth of AI technology has given rise to concerns related to bias, lack of transparency, and the need for clear accountability mechanisms. In this exploration, we delve into the intricate ethical landscape of AI, examining issues such as bias and fairness, lack of transparency, and the challenges associated with accountability. To address these concerns, we propose strategies for transparency, including the implementation of Explainable AI (XAI), advocating for open data sharing, and embracing ethical AI frameworks. Furthermore, we explore strategies to promote fairness in AI algorithms, emphasizing the importance of fairness metrics, diverse training data, and continuous monitoring for iterative improvement. Additionally, the paper delves into strategies to ensure accountability in AI development, considering regulatory measures, ethical AI governance, and the incorporation of human-in-the-loop approaches. To provide practical insights, case studies and real-world examples are analyzed to distill lessons learned and best practices. The paper concludes with a comprehensive overview of the proposed strategies, emphasizing the importance of balancing innovation with ethical responsibility in the evolving landscape of AI development. This work contributes to the ongoing discourse on AI ethics, offering a roadmap for navigating the challenges and fostering responsible AI development practices.

Navigating the Future: AI’s Revolutionary Drive in the Auto Industry

The dawn of the 21st century has witnessed the automotive industry at the threshold of a paradigm shift, propelled by the integration of Artificial Intelligence (AI). This comprehensive exploration delves into the multifaceted role of AI as a harbinger of change, reshaping the contours of automotive design, manufacturing, and user experience. The paper commences with a historical overview, charting the ascent of AI from conceptual frameworks to its pervasive application across the automotive sector. A thorough literature review synthesizes seminal research, underscoring the operational efficiencies achieved, the impetus for innovation, and the challenges encountered in data stewardship and ethical governance. AI’s imprint on the industry is indelible, enhancing operational efficiency in production, quality control, and assembly, while simultaneously serving as the crucible for the development of connected and autonomous vehicles. The narrative, however, is nuanced by the challenges that accompany this technological march forward. The discourse addresses the intricacies related to data availability, quality, and system integration, alongside the ethical quandaries and regulatory conundrums that accompany the increased autonomy of AI-driven vehicles. Peering into the future, the paper proffers informed predictions about the trajectory of AI within the automotive industry, positing its potential to redefine mobility. Recommendations for future research and development are articulated, emphasizing the need to bridge identified literature gaps, particularly in the ethical deployment of AI and the pursuit of sustainable practices. In summation, the paper reaffirms the pivotal role of AI in the automotive industry, not solely as a catalyst for technological advancement but as a beacon for safety, efficiency, and environmental stewardship. The prospective landscape of AI in automotive paints a portrait of an interconnected, intelligent transportation ecosystem that promises to redefine the human experience of mobility. The conclusion encapsulates the essence of the discourse, advocating for a responsible embrace of AI that aligns with societal values and enhances the collective well- being

Artificial intelligence development and dissemination impact on the sports industry labor market.

The objective of this study is to explore the impact of artificial intelligence (AI) development on the sports industry labor market, the ways in which AI has influenced the demand for labor, created new job opportunities, and impacted existing job roles. It refers to the inductive approach in the spirit technological determinism theory. It is based on the literature review and written qualitative, semi-structured interviews (N = 14) with sports human resources, management, and technology professionals (purposive sampling). Analysis involved inductive coding and line-by-line analytics of the data. The labor market implications of AI in the sports industry are multifaceted. New job roles are likely to emerge, demanding a blend of AI expertise, data-analysis skills, and sports domain knowledge. Professionals in roles such as sports data analysts and marketing experts may find increasing opportunities. However, certain jobs undergo transformation as AI automates routine tasks. It requires individuals to upskill or transition to roles that require a deeper understanding of AI. This necessitates the creation of responsibilities focused on ethical AI governance and oversight. It is important to research the impact of AI dissemination on the sports industry labor market in a holistic manner because the effects of AI are complex and far-reaching. While there are potential benefits to the implementation of AI, there are also potential risks and challenges that need to be addressed, the implementation of AI in the sports industry could have broader social and ethical implications that need to be considered.

Developing a Framework for Self-regulatory Governance in Healthcare AI Research: Insights from South Korea

This paper elucidates and rationalizes the ethical governance system for healthcare AI research, as outlined in the ‘Research Ethics Guidelines for AI Researchers in Healthcare’ published by the South Korean government in August 2023. In developing the guidelines, a four-phase clinical trial process was expanded to six stages for healthcare AI research: preliminary ethics review (stage 1); creating datasets (stage 2); model development (stage 3); training, validation, and evaluation (stage 4); application (stage 5); and post-deployment monitoring (stage 6). Researchers identified similarities between clinical trials and healthcare AI research, particularly in research subjects, management and regulations, and application of research results. In the step-by-step articulation of ethical requirements, this similarity benefits from a reliable and flexible use of existing research ethics governance resources, research management, and regulatory functions. In contrast to clinical trials, this procedural approach to healthcare AI research governance effectively highlights the distinct characteristics of healthcare AI research in research and development process, evaluation of results, and modifiability of findings. The model exhibits limitations, primarily in its reliance on self-regulation and lack of clear delineation of responsibilities. While formulated through multidisciplinary deliberations, its application in the research field remains untested. To overcome the limitations, the researchers’ ongoing efforts for educating AI researchers and public and the revision of the guidelines are expected to contribute to establish an ethical research governance framework for healthcare AI research in the South Korean context in the future.

CSR performance of dual-class firms in different life cycle stages

ABSTRACT This paper investigates the relationship between corporate social responsibility (CSR) performance and different phases within a firm’s life cycle. Drawing on a sample of U.S. public firms spanning the period from 1991 to 2018, our empirical findings reveal distinct patterns: Younger firms with dual-class equity structures tend to allocate fewer resources to CSR initiatives compared to their single-class counterparts. However, as firms progress into the mature phase of their life cycle, dual-class firms exhibit superior CSR performance compared to single-class firms. Furthermore, we also observe that CEO compensation in dual-class firms tends to be higher during the mature phase. Those findings make a substantial contribution to the fields of agency theory, corporate governance, and business ethics.

AI-Driven HR Analytics: Unleashing the Power of HR Data Management

The quickly changing field of human resources (HR) starts by highlighting the importance of HR analytics, its development, and the benefits it provides for making data-driven decisions. The foundation for efficient HR analytics is laid out in this article, which also emphasizes the significance of employee data management, which includes the governance, quality assurance, and collection, storage, and collection of all types of employee data. AI’s revolutionary significance is highlighted, demonstrating how AI improves the processing and analysis of employee data. The AI-driven HR Analytics Workflow breaks down the crucial phases needed, while real-world best practices highlight the potential of this technology. A focus is placed on ethical issues, including bias, data privacy, and responsible AI governance. In addition, the study discusses future trends and how HR professionals will change in an AI-driven HR environment. It encourages organizations to embrace AI-driven HR analytics as a strategic tool for HR excellence, highlighting the importance of responsible data use in shaping the future of HR practices.

Skeletons in the closet: How and when internal and external corporate social responsibility affect employees' internal whistleblowing behaviors

AbstractThe micro‐level corporate social responsibility (CSR) literature has underscored the economic benefits of an organization's CSR investments, such as bolstering employees' organizational commitment and improving work performance. Yet, research on the potential influence of CSR in fostering socially oriented outcomes among employees has been rather scarce. This study aims to investigate the influence of CSR on employees' internal whistleblowing behaviors and the underlying mechanisms. A three‐time‐point survey was distributed across the service, manufacturing, construction, and financial insurance industries in Chinese enterprises. The findings reveal that both internal and external CSR positively impact employees' internal whistleblowing behaviors through the mediating role of moral courage, with internal CSR demonstrating a stronger effect. Organizational loyalty positively moderates the mediating role of moral courage in the relationship between internal CSR/external CSR and internal whistleblowing behaviors. Theoretically, this study presents a pioneering endeavor in establishing the link between CSR and employees' internal whistleblowing behaviors, underscoring the significant role that employees' perceptions of CSR play in enhancing societal well‐being and ethical governance. Besides contributing to the micro‐level CSR literature, this study also provides practical insights for organizations seeking to leverage CSR as a tool for promoting employees' ethical conduct.

The Legal and Political Implications of AI Bias: An International Comparative Study

Purpose: "The Legal and Political Implications of AI Bias: An International Comparative Study" extensively navigates the intricate terrain of AI governance, with a specific focus on the ethical challenges arising from bias in AI systems. The purpose of this study is to underscore the urgent need for robust regulatory frameworks to address issues of bias, discrimination, and fairness within the realm of AI technologies.
 Materials and Methods: The research methodology involved a comprehensive analysis of international perspectives on AI bias. This entailed examining existing literature, legal frameworks, and political dynamics surrounding AI governance in various countries. Comparative analysis was conducted to elucidate the diverse approaches adopted by different nations to tackle AI bias and unravel the corresponding legal and political consequences.
 Findings: The study highlighted the inherent risks associated with biased algorithms and stressed the paramount importance of proactively detecting and mitigating bias to prevent discrimination and promote fairness in AI systems. Additionally, it advocated for comprehensive measures such as risk management strategies, conformity assessments for high-risk AI applications, and the careful handling of sensitive data to identify and rectify biases that could lead to discriminatory outcomes.
 Implication to Theory, Practice and Policy: The study was informed by theories of ethical governance and legal frameworks in AI development and deployment. It was validated through the comparative analysis of international perspectives, which provided insights into the effectiveness of different regulatory approaches in addressing AI bias. Recommendations to practitioners include implementing risk management strategies, conducting conformity assessments for high-risk AI applications, and ensuring the careful handling of sensitive data to identify and rectify biases. Practitioners are urged to prioritize ethical considerations and advocate for responsible deployment practices to mitigate AI bias effectively. Recommendations to policymakers emphasize the need to prioritize ethical considerations and advocate for responsible deployment practices in AI governance. Policymakers are urged to develop robust regulatory frameworks that promote transparency, accountability, and inclusivity in AI development and deployment to build a more equitable and trustworthy AI ecosystem.
 In essence, the study provides crucial insights into the complex interplay between legal frameworks, political dynamics, and ethical considerations in addressing AI bias on a global scale. It paves the way for the establishment of fair and unbiased AI systems that benefit society as a whole.

‶Don’t Say Neigh, Say Yay”

‶Don’t Say Neigh, Say Yay”

Diplomacy in the Age of AI: Challenges and Opportunities

As artificial intelligence (AI) continues to permeate various aspects of society, its impact on diplomacy and international relations becomes increasingly profound. This paper explores the challenges and opportunities presented by the intersection of diplomacy and AI. It examines how AI technologies are reshaping traditional diplomatic practices, influencing decision-making processes, and altering power dynamics among nation-states. Additionally, it discusses the ethical implications and governance frameworks necessary to navigate this evolving landscape. Despite the challenges, AI offers numerous opportunities for enhancing diplomatic efforts, fostering collaboration, and addressing global challenges in a more efficient and effective manner. By understanding and harnessing the potential of AI, diplomats can adapt to the changing landscape of international relations and leverage technology to advance diplomatic objectives.

Diplomacy in the Age of AI: Challenges and Opportunities

As artificial intelligence (AI) continues to permeate various aspects of society, its impact on diplomacy and international relations becomes increasingly profound. This paper explores the challenges and opportunities presented by the intersection of diplomacy and AI. It examines how AI technologies are reshaping traditional diplomatic practices, influencing decision-making processes, and altering power dynamics among nation-states. Additionally, it discusses the ethical implications and governance frameworks necessary to navigate this evolving landscape. Despite the challenges, AI offers numerous opportunities for enhancing diplomatic efforts, fostering collaboration, and addressing global challenges in a more efficient and effective manner. By understanding and harnessing the potential of AI, diplomats can adapt to the changing landscape of international relations and leverage technology to advance diplomatic objectives.

Governance As a Delicate Balance: On the Concept of Luṭf in Islamic Mirrors for Princes

Abstract This article is concerned with Islamic works on political ethics as a practical guide for competent governance. The article primarily focuses on the concept of luṭf (gentleness) in the works of al-ādāb al-sulṭāniyya (the ethics of government, i.e., Islamic mirrors for princes). The main argument is that Muslim writers of al-ādāb al-sulṭāniyya literature warned rulers against depending on coercive power as the basic mode of governance. Instead, the majority emphasized that the most practical way to govern a state was to practice governance based on a balance between rewards and punishments. Based on their advice, rulers should avoid cruelty in favor of an interdependent political order based on social cooperation between the different classes. The article first explains the concept of luṭf and the importance of avoiding cruelty. Second, the article relates how luṭf operates in the realm of public policy, ruler-people relations, and the responsibility of rulers. Finally, the article highlights how luṭf dictates a functional elite structure based on loyalty and mutual respect. The methodology of this article relies on primary sources, mostly analyzing the presence of luṭf as a concept and the details of governance surrounding the concept. The article seeks to show the intricacies of the state through understanding the concept of luṭf and its relation with practicing politics in a cohesive Muslim social structure.

Strategic Leadership and Diplomatic Agility: A Comparative Analysis of Conflict Resolution in South Africa and Qatar

This research delves into the dynamic interplay between strategic leadership and diplomatic agility in conflict resolution, juxtaposing Nelson Mandela's transformational leadership in post-apartheid South Africa with Qatar's diplomatic navigation through the 2017 geopolitical blockade. It aims to uncover how leadership dynamics are crucial in guiding nations through conflicts and crises in diverse geopolitical landscapes, employing a qualitative comparative methodology that includes case study analysis and thematic literature review to investigate leadership virtues, diplomatic engagements, and the strategic use of international law. By comparing Mandela's commitment to reconciliation and unity with Qatar's resilience and economic ingenuity, this study seeks to articulate core principles underpinning strategic leadership and diplomatic agility, highlighting their applicability across various geopolitical challenges. Anticipated outcomes include the identification of key leadership traits such as ethical governance, strategic foresight, and the ability to foster unity, alongside effective diplomatic engagement and adherence to international norms. This investigation enriches the academic discourse on conflict resolution by providing insights into the synergy between leadership and diplomacy in managing complex disputes, underscoring the adaptability of leadership strategies in changing geopolitical dynamics, and setting a foundation for future leadership efforts aimed at achieving global peace, stability, and progress. Furthermore, it opens avenues for further scholarly inquiry into leadership dynamics in conflict resolution, enhancing the academic understanding and offering practical guidance for policymakers and leaders, thereby emphasizing the critical importance of visionary leadership and diplomatic finesse in achieving sustainable resolutions to international conflicts.

Applications and implementation of generative artificial intelligence in cardiovascular imaging with a focus on ethical and legal considerations: what cardiovascular imagers need to know!

Abstract Machine learning (ML) and deep learning (DL) have potential applications in medicine. This overview explores the applications of AI in cardiovascular imaging, focusing on echocardiography, cardiac MRI (CMR), coronary CT angiography (CCTA), and CT morphology and function. AI, particularly DL approaches like convolutional neural networks, enhances standardization in echocardiography. In CMR, undersampling techniques and DL-based reconstruction methods, such as variational neural networks, improve efficiency and accuracy. ML in CCTA aids in diagnosing coronary artery disease, assessing stenosis severity, and analyzing plaque characteristics. Automatic segmentation of cardiac structures and vessels using AI is discussed, along with its potential in congenital heart disease diagnosis and 3D printing applications. Overall, AI integration in cardiovascular imaging shows promise for enhancing diagnostic accuracy and efficiency across modalities. The growing use of Generative Adversarial Networks in cardiovascular imaging brings substantial advancements but raises ethical concerns. The “black box” problem in DL models poses challenges for interpretability crucial in clinical practice. Evaluation metrics like ROC curves, image quality, clinical relevance, diversity, and quantitative performance assess GAI models. Automation bias highlights the risk of unquestioned reliance on AI outputs, demanding careful implementation and ethical frameworks. Ethical considerations involve transparency, respect for persons, beneficence, and justice, necessitating standardized evaluation protocols. Health disparities emerge if AI training lacks diversity, impacting diagnostic accuracy. AI language models, like GPT-4, face hallucination issues, posing ethical and legal challenges in healthcare. Regulatory frameworks and ethical governance are crucial for fair and accountable AI. Ongoing research and development are vital to evolving AI ethics.

Ethical considerations in AI-enhanced medical decision support systems: A review

As Artificial Intelligence (AI) continues to play an increasingly pivotal role in medical decision support systems, the ethical implications of its integration into healthcare practices demand comprehensive examination. This review delves into the ethical considerations surrounding AI-enhanced medical decision support systems, aiming to provide insights into the challenges, existing frameworks, exemplary practices, and emerging trends in this rapidly evolving field. The significance of ethical considerations is underscored by the patient-centric focus, emphasizing the impact of AI on patient outcomes and the delicate balance between technological advancements and patient welfare. Trust and transparency emerge as critical pillars, exploring the role of trust in medical decision-making and the imperative of ensuring transparency in AI algorithms to foster confidence among healthcare professionals and patients. Ethical challenges, including privacy and confidentiality concerns, biases in AI algorithms, and issues related to informed consent, are thoroughly examined. Strategies for safeguarding patient data, mitigating biases, and transparently communicating with patients are explored to address these challenges. The role of accountability and responsibility is delineated, defining the ethical responsibilities of both healthcare professionals and AI developers. The review surveys existing ethical frameworks in healthcare AI and evaluates their applicability and effectiveness. Additionally, it highlights recent proposals for ethical guidelines, emphasizing the need to integrate ethical considerations into the entire development life cycle of AI-enhanced medical decision support systems. Case studies and exemplary practices from healthcare institutions implementing ethical AI serve to illustrate real-world applications and offer insights into best practices. The evolving landscape of ethical AI research is explored, showcasing ongoing initiatives and potential innovations that hold promise for addressing ethical challenges in the future. This review underscores the paramount importance of ethical considerations in the integration of AI into medical decision support systems. It provides a comprehensive overview of current challenges, existing frameworks, exemplary practices, and emerging trends, emphasizing the ongoing need for vigilance and ethical governance to ensure the responsible and beneficial deployment of AI in healthcare.

News and ESG investment criteria: What’s behind it?

News written in the press about different companies generates consumer feelings that can condition the reputation of these companies and, consequently, their financial results. One of the practices that might improve a company’s reputation is the Environmental, Social and Governance (ESG) investment criteria. In this research, using Natural Language Processing techniques like Sentiment Analysis and Word2Vec, we detected those ESG-related terms that the written press uses in news articles about companies. Thus, we have been able to discover and analyze those terms that improve sympathy toward companies, and those that worsen it. Our findings show that those terms related to sustainable development, good social practices and ethical governance improve the general public’s opinion of a company, while those related to greenwashing and socialwashing worsen it. Therefore, this methodology is valid for enabling companies to detect those terms that improve or worsen their reputation, and thus help them make decisions that improve their image.

Human Capital Management in the Context of Artificial Intelligence

Artificial Intelligence (AI) stands as a transformative force with profound implications across various domains, including human capital management (HCM), especially in the field of education. In education, AI's integration promises to revolutionize traditional teaching methods, offering adaptive learning experiences and personalized feedback while challenging traditional pedagogical structures. Yet, risks include widening disparities in access to quality education and the erosion of human connection in learning environments, highlighting the need for ethical AI governance and socio-culturally sensitive approaches. Ultimately, the effective utilization of AI in education necessitates a nuanced understanding of its opportunities and dangers within the socio-cultural contexts in which they operate, fostering equitable and inclusive outcomes.

Artificial Intelligence and Inequality: Challenges and Opportunities

Integrating artificial intelligence (AI) technologies into various aspects of society has sparked both excitement and concern regarding its potential impact on inequality. This abstract provides an overview of the key issues surrounding AI and inequality, exploring the challenges and opportunities arising from the widespread adoption of AI systems. Firstly, we examine how AI technologies have the potential to exacerbate existing inequalities across various domains, including labor markets, education, healthcare, and access to services. AI-driven automation may lead to job displacement and wage polarization, widening the gap between high-skilled and low-skilled workers. Moreover, algorithmic biases embedded in AI systems can perpetuate discrimination and inequity, particularly against marginalized communities. However, alongside these challenges, AI also presents opportunities to address inequality and promote inclusivity. AI-powered innovations have the potential to enhance efficiency, accessibility, and affordability in sectors such as healthcare, education, and financial services, thereby reducing disparities in access to essential resources and opportunities. Additionally, initiatives focused on ethical AI development and responsible AI governance can mitigate the negative impacts of AI on inequality by promoting fairness, transparency, and accountability in algorithmic decision-making processes. In conclusion, while AI has the potential to both exacerbate and mitigate inequality, its ultimate impact depends on the choices we make in designing, deploying, and governing AI systems. By prioritizing equity, social justice, and human welfare in AI development and implementation, we can harness the transformative power of AI to create a more equitable and inclusive society.

Sustainability reporting in focus: analysing Spanish transposition of the Non-Financial Reporting European Directive in the agri-food sector

AbstractIn recent decades, the business world has undergone a paradigm shift, prioritizing social and environmental considerations over the exclusive pursuit of economic profits. Corporate social responsibility has become an essential practice, and Sustainability reports (SRs) play a crucial role in facilitating the disclosure of these practices. In the European Union (EU), Directive 2014/95/EU (Non-Financial Reporting Directive—NFRD) was enacted to compel companies to disclose information on matters addressing social, environmental, ethical, and corporate governance aspects. Spain transposed this directive through Law 11/2018, considered one of the strictest in the EU. This study aims to explore and analyse its impact on the agri-food sector. For this purpose, the SRs of 16 agri-food companies that used the Global reporting initiative standard before and after the enforcement of Law 11/2018 were evaluated applying content analysis along with repeated measures ANOVA. The results show a low level of disclosure for both periods and that Law 11/2018 does not succeed in improving the level of disclosure. This suggests that legislators should develop regulations that ensure the usefulness of the disclosed information and that companies should be more involved in the preparation of SRs.