Ethical Issues of Artificial Intelligence (AI) in the Healthcare

Artificial intelligence (AI)-based technology has achieved many great things, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data biases, data security, data privacy, and ethical problems of AI-based technology pose significant risks for users, developers, humanity, and societies. As AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. Even though the concept of “machine ethics” was proposed around 2006, AI ethics is still in the infancy stage. AI ethics is the field related to the study of ethical issues in AI. To address AI ethics, one needs to consider the ethics of AI and how to build ethical AI. Ethics of AI studies the ethical principles, rules, guidelines, policies, and regulations that are related to AI. Ethical AI is an AI that performs and behaves ethically. One must recognize and understand the potential ethical and moral issues that may be caused by AI to formulate the necessary ethical principles, rules, guidelines, policies, and regulations for AI (i.e., Ethics of AI). With the appropriate ethics of AI, one can then build AI that exhibits ethical behavior (i.e., Ethical AI). This paper will discuss AI ethics by looking at the ethics of AI and ethical AI. What are the perceived ethical and moral issues with AI? What are the general and common ethical principles, rules, guidelines, policies, and regulations that can resolve or at least attenuate these ethical and moral issues with AI? What are some of the necessary features and characteristics of an ethical AI? How to adhere to the ethics of AI to build ethical AI?

In an era marked by rapid technological advancement, the convergence of corporate governance and artificial intelligence (AI) ethics has emerged as a pivotal concern for modern businesses. As AI technologies become deeply embedded in decision-making processes, the risks of ethical violations, bias, lack of transparency, and accountability have intensified. While AI promises significant improvements in efficiency, innovation, and strategic agility, these benefits can only be realized within a robust ethical and governance framework. This paper explores how corporate governance can be strategically aligned with AI ethics to promote responsible innovation and uphold societal trust. The research delves into the intersection of governance structures, ethical principles, and AI applications to propose a comprehensive strategic framework that ensures ethical decision-making in business. It examines the roles and responsibilities of boards of directors, executive leadership, and key stakeholders in fostering an ethical AI culture. Emphasis is placed on principles such as transparency, accountability, fairness, privacy, and inclusivity, all of which are essential to maintain corporate integrity and public confidence. This study employs a multidisciplinary approach, integrating insights from corporate governance theory, ethical philosophy, AI regulatory policies, and business case studies. The paper also investigates existing challenges businesses face in implementing AI ethically, including regulatory ambiguity, insufficient oversight mechanisms, and potential conflicts of interest. Ultimately, this paper offers strategic recommendations for integrating AI ethics into corporate governance frameworks, including the adoption of ethical guidelines, establishment of AI ethics committees, continuous training for employees, and fostering stakeholder engagement. These measures aim to ensure that organizations not only comply with legal standards but also go beyond compliance to embrace ethical leadership in the age of AI. By presenting a robust strategic framework, this research contributes to ongoing discussions on ethical AI and responsible corporate governance, encouraging businesses to adopt proactive, transparent, and inclusive strategies that align technological innovation with societal values.

Artificial Intelligence (AI) is becoming more and more important especially in datacentric fields, such as biomedical research and biobanking. However, AI does not only offer advantages and promising benefits, but brings about also ethical risks and perils. In recent years, there has been growing interest in AI ethics, as reflected by a huge number of (scientific) literature dealing with the topic of AI ethics. The main objectives of this review are: (1) to provide an overview about important (upcoming) AI ethics regulations and international recommendations as well as available AI ethics tools and frameworks relevant to biomedical research, (2) to identify what AI ethics can learn from findings in ethics of traditional biomedical research - in particular looking at ethics in the domain of biobanking, and (3) to provide an overview about the main research questions in the field of AI ethics in biomedical research. We adopted a modified thematic review approach focused on understanding AI ethics aspects relevant to biomedical research. For this review, four scientific literature databases at the cross-section of medical, technical, and ethics science literature were queried: PubMed, BMC Medical Ethics, IEEE Xplore, and Google Scholar. In addition, a grey literature search was conducted to identify current trends in legislation and standardization. More than 2,500 potentially relevant publications were retrieved through the initial search and 57 documents were included in the final review. The review found many documents describing high-level principles of AI ethics, and some publications describing approaches for making AI ethics more actionable and bridging the principles-to-practice gap. Also, some ongoing regulatory and standardization initiatives related to AI ethics were identified. It was found that ethical aspects of AI implementation in biobanks are often like those in biomedical research, for example with regards to handling big data or tackling informed consent. The review revealed current 'hot' topics in AI ethics related to biomedical research. Furthermore, several published tools and methods aiming to support practical implementation of AI ethics, as well as tools and frameworks specifically addressing complete and transparent reporting of biomedical studies involving AI are described in the review results. The review results provide a practically useful overview of research strands as well as regulations, guidelines, and tools regarding AI ethics in biomedical research. Furthermore, the review results show the need for an ethical-mindful and balanced approach to AI in biomedical research, and specifically reveal the need for AI ethics research focused on understanding and resolving practical problems arising from the use of AI in science and society.

The proposed work aims to develop an ethical framework for the implementation of artificial intelligence (AI). The present work changes the discussion from 'What is AI ethics' to 'how AI system developers can implement AI ethics'. The current work deploys cybernetics principles to address the challenges pertaining to AI ethics implementation. With the help of two pillar elements of cybernetics, i.e., man and machine, AI ethics principles have been elucidated in the present work. The study demonstrates that cybernetics provides a different dimension to implement AI ethics principles and dispenses a basis to deploy already existing AI ethics principles. The combination of cybernetics theory and AI ethics principles serves as a firm foundation for implementing AI ethics. The present work provides a comparative study of IBM principles for AI ethics, Japanese Society of Artificial Intelligence's (JSAI's) AI ethics principles, and the proposed cybernetics-based AI ethics framework to provide holistic visualisation.

The proposed work aims to develop an ethical framework for the implementation of artificial intelligence (AI). The present work changes the discussion from 'What is AI ethics' to 'how AI system developers can implement AI ethics'. The current work deploys cybernetics principles to address the challenges pertaining to AI ethics implementation. With the help of two pillar elements of cybernetics, i.e., man and machine, AI ethics principles have been elucidated in the present work. The study demonstrates that cybernetics provides a different dimension to implement AI ethics principles and dispenses a basis to deploy already existing AI ethics principles. The combination of cybernetics theory and AI ethics principles serves as a firm foundation for implementing AI ethics. The present work provides a comparative study of IBM principles for AI ethics, Japanese Society of Artificial Intelligence's (JSAI's) AI ethics principles, and the proposed cybernetics-based AI ethics framework to provide holistic visualisation.

E-commerce data security is influenced by the three elements; Explainable AI (XAI), Federated Learning, and Artificial Intelligence (AI) Ethics. The objective of Explainable AI is to enhance the transparency of AI systems and their explainability to promote human oversight and foster confidence. Federated Learning (FL) provides a decentralized approach by training Machine Learning (ML) models to secure user data confidentiality and privacy. Instead of exchanging local data, model constraints from local ML models are pooled and shared as a method of collaborative learning among devices and organizations. The bias, fairness, and the effects on society are a few moral issues surrounding the use of AI covered in AI Ethics. It discusses moral regulations, guidelines, and policies that support the development and application of moral AI. This chapter aims to give insight into the technological and ethical aspects of AI advances concerning AI-ML for data security in E-Commerce, with a focus on the importance of privacy, accountability, and transparency in AI systems.

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

Trustworthy AI Implementation (TAII) Framework for AI Systems

Technological support to foster students’ artificial intelligence ethics: An augmented reality-based contextualized dilemma discussion approach

Incorporating interdisciplinary perspectives is seen as an essential step towards enhancing artificial intelligence (AI) ethics. In this regard, the field of arts is perceived to play a key role in elucidating diverse historical and cultural narratives, serving as a bridge across research communities. Most of the works that examine the interplay between the field of arts and AI ethics concern digital artworks, largely exploring the potential of computational tools in being able to surface biases in AI systems. In this paper, we investigate a complementary direction--that of uncovering the unique socio-cultural perspectives embedded in human-made art, which in turn, can be valuable in expanding the horizon of AI ethics. Through semi-structured interviews across sixteen artists, art scholars, and researchers of diverse Indian art forms like music, sculpture, painting, floor drawings, dance, etc., we explore how {\it non-Western} ethical abstractions, methods of learning, and participatory practices observed in Indian arts, one of the most ancient yet perpetual and influential art traditions, can shed light on aspects related to ethical AI systems. Through a case study concerning the Indian dance system (i.e. the {\it `Natyashastra'}), we analyze potential pathways towards enhancing ethics in AI systems. Insights from our study outline the need for (1) incorporating empathy in ethical AI algorithms, (2) integrating multimodal data formats for ethical AI system design and development, (3) viewing AI ethics as a dynamic, diverse, cumulative, and shared process rather than as a static, self-contained framework to facilitate adaptability without annihilation of values (4) consistent life-long learning to enhance AI accountability

Background: Questions surrounding the ethics of artificial intelligence (AI) have been debated for decades [1]. However, in recent years there have been multiple initiatives, scholarly reviews, and policy documents to identify and define ethical issues in play [2]. The efforts to bring high-level principles to applicable practice are complex and can be lost in translation [3]. Moreover, a call to be proactive, rather than reactive, stems from a deduction of intentions behind responsible innovation, value-centric design principles, education efforts, and representative data management techniques. Contemporary applications of AI are complex and difficult to explain, edit, and deal with once integrated in a natural system [4] [5]. Therefore, the analysis conducted within this systematic literature review (SLR) will clarify methods to promote and engage practice on the front end of ethical and responsible AI. As such, the research question is explored: How does each helix in the Quintuple Innovation model address responsible and ethical AI technology with anticipatory or proactive approaches? Methods: To conduct this ongoing research, an adaptation of the PRISMA framework and Hess & Fore's 2017 methodological approach guides the SLR [6] [7]. We included journal articles that were written in English and published between 2018-2023. The collected studies aim to examine how academic scholarship approaches to responsible AI within academia, government, industry, civil society, or the natural environment (the Quintuple Helix). The Web of Science, Google Scholar, and PhilPapers databases were used to identify a set of prominent publications in this field: AI & Society, Nature Machine Intelligence, Minds and Machines, IEEE Transactions on Technology and Society, AI and Ethics, Science and Engineering Ethics, and Communications of the ACM. A key limitation of this study is that it cannot gather the entirety of literature written about the topics of proactively promoting ethical AI due to the vast size and definitional complexity of the associated fields. These inclusion criteria allow the researchers to manage the data and draw meaningful insights from the most current thinking that is reflected in the rapid development of AI innovation we see today. Results and discussion: This poster will present preliminary results and the theoretical framework that guided the qualitative coding process. Additionally, this poster will serve as a forum to collect experts' opinions about what they would like to see from this SLR dataset, and how we can incorporate those elements into our coding. As a result, this data will be able to inform future work to investigate multiple gaps in the literature. For instance, U.S. Government work not protected by U.S. copyright this study will result in a theoretical framework that identifies proactive approaches to responsible and sustainable AI aligned with the five sectors for innovation. Inspired from [8], the effects of investments in education, and other sectors, will be mapped as a chain of responsible AI innovation across all innovation sectors. Finally, we can draw informed conclusions about the use and misuse of experts in AI, ethics, education, and policy. By working towards these objectives, we can see how the interdisciplinary field has made (or not made) a collective effort toward promoting responsible AI-filling a gap in the literature that highlights proactive approaches, rather than reactive. In conclusion, this data will inform experts across multiple domains about how to approach and organize a concerted effort to promote ethical and responsible AI in a pragmatic way.

This paper analyzes where Artificial Intelligence (AI) ethics research fails and breaks down the dangers of well-intentioned but ultimately performative ethics research. A large majority of AI ethics research is criticized for not providing a comprehensive analysis of how AI is interconnected with sociological systems of oppression and power. Our work contributes to the handful of research that presents intersectional, Western systems of oppression and power as a framework for examining AI ethics work and the complexities of building less harmful technology; directly connecting technology to named systems such as capitalism and classism, colonialism, racism and white supremacy, patriarchy, and ableism. We then explore current AI ethics rhetoric’s effect on the AI ethics domain. We conclude by providing an applied example to contextualize intersectional systems of oppression and AI interventions in the US justice system and present actionable steps for AI practitioners to participate in a less performative, critical analysis of AI. This article appears in the AI & Society track.

Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research. This study conducts a comprehensive bibliometric analysis of the AI ethics literature over the past two decades. The analysis reveals a discernible tripartite progression, characterized by an incubation phase, followed by a subsequent phase focused on imbuing AI with human-like attributes, culminating in a third phase emphasizing the development of human-centric AI systems. After that, they present seven key AI ethics issues, encompassing the Collingridge dilemma, the AI status debate, challenges associated with AI transparency and explainability, privacy protection complications, considerations of justice and fairness, concerns about algocracy and human enfeeblement, and the issue of superintelligence. Finally, they identify two notable research gaps in AI ethics regarding the large ethics model (LEM) and AI identification and extend an invitation for further scholarly research.

This paper analyzes where Artificial Intelligence (AI) Ethics research fails and breaks down the dangers of well-intentioned, but ultimately performative ethics research. A large majority of AI ethics research is critiqued for lacking a comprehensive analysis of how AI is interconnected with sociological systems of oppression and power. Our work contributes to the handful of research that presents intersectional, Western systems of oppression and power as a framework for examining AI ethics work and the complexities of building less harmful technology; directly connecting technology to named systems such as capitalism and classism, colonialism, racism and white supremacy, patriarchy, and ableism. We then explore current AI ethics rhetoric's effect on the AI ethics domain and AI regulation. In conclusion, we provide an applied example to contextualize intersectional systems of oppression and AI interventions in the U.S. justice system and present actionable steps for AI practitioners to participate in a less performative, critical analysis of AI.

This paper proposes to generate awareness for developing Artificial intelligence (AI) ethics by transferring knowledge from other fields of applied ethics, particularly from business ethics, stressing the role of organizations and processes of institutionalization. With the rapid development of AI systems in recent years, a new and thriving discourse on AI ethics has (re-)emerged, dealing primarily with ethical concepts, theories, and application contexts. We argue that business ethics insights may generate positive knowledge spillovers for AI ethics, given that debates on ethical and social responsibilities have been adopted as voluntary or mandatory regulations for organizations in both national and transnational contexts. Thus, business ethics may transfer knowledge from five core topics and concepts researched and institutionalized to AI ethics: (1) stakeholder management, (2) standardized reporting, (3) corporate governance and regulation, (4) curriculum accreditation, and as a unified topic (5) AI ethics washing derived from greenwashing. In outlining each of these five knowledge bridges, we illustrate current challenges in AI ethics and potential insights from business ethics that may advance the current debate. At the same time, we hold that business ethics can learn from AI ethics in catching up with the digital transformation, allowing for cross-fertilization between the two fields. Future debates in both disciplines of applied ethics may benefit from dialog and cross-fertilization, meant to strengthen the ethical depth and prevent ethics washing or, even worse, ethics bashing.