Applying AI Ethics Tools in Mining Industry Organizations

This paper argues that the field of artificial intelligence (AI) ethics needs to give more attention to the values and interests of nonhumans such as other biological species and the AI itself. It documents the extent of current attention to nonhumans in AI ethics as found in academic research, statements of ethics principles, and select projects to design, build, apply, and govern AI. It finds that the field of AI ethics gives limited and inconsistent attention to nonhumans, with the main activity being a line of research on the moral status of AI. The paper argues that nonhumans merit moral consideration, meaning that they should be actively valued for their own sake and not ignored or valued just for how they might benefit humans. Finally, it explains implications of moral consideration of nonhumans for AI ethics research and practice, including for the content of AI ethics principles, the selection of AI projects, the accounting of inadvertent effects of AI systems such as via their resource and energy consumption and potentially certain algorithmic biases, and the research challenge of incorporating nonhuman interests and values into AI system design. The paper does not take positions on which nonhumans to morally consider or how to balance the interests and values of humans vs. nonhumans. Instead, the paper makes the more basic argument that the field of AI ethics should move from its current state of affairs, in which nonhumans are usually ignored, to a state in which nonhumans are given more consistent and extensive moral consideration.

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.

This study is focused on the ethics of Artificial Intelligence and its application in the United States, the paper highlights the impact AI has in every sector of the US economy and multiple facets of the technological space and the resultant effect on entities spanning businesses, government, academia, and civil society. There is a need for ethical considerations as these entities are beginning to depend on AI for delivering various crucial tasks, which immensely influence their operations, decision-making, and interactions with each other. The adoption of ethical principles, guidelines, and standards of work is therefore required throughout the entire process of AI development, deployment, and usage to ensure responsible and ethical AI practices. Our discussion explores eleven fundamental 'ethical principles' structured as overarching themes. These encompass Transparency, Justice, Fairness, Equity, Non-Maleficence, Responsibility, Accountability, Privacy, Beneficence, Freedom, Autonomy, Trust, Dignity, Sustainability, and Solidarity. These principles collectively serve as a guiding framework, directing the ethical path for the responsible development, deployment, and utilization of artificial intelligence (AI) technologies across diverse sectors and entities within the United States. The paper also discusses the revolutionary impact of AI applications, such as Machine Learning, and explores various approaches used to implement AI ethics. This examination is crucial to address the growing concerns surrounding the inherent risks associated with the widespread use of artificial intelligence.

This study is motivated by the growing discourse on artificial intelligence (AI) ethics that has not yet been fully operationalized in practice, particularly in the health and education sectors in the era of generative AI. It aims to synthesize the literature on the operationalization of AI ethics in order to propose a new governance model that is more adaptive and context-sensitive. A structured thematic synthesis was conducted on peer-reviewed studies published between 2020 and 2025. The analysis reveals a central paradox: a strong global consensus on the principles of justice, transparency, accountability, and privacy stands in stark contrast to significant gaps in their implementation. The emerging challenges are highly context-dependent: in the health sector, they center on technical safety and relational accountability, whereas in the education sector they are more closely related to epistemic integrity and equitable access. The advent of generative AI exacerbates these issues by introducing new systemic risks, such as large-scale hallucinations and an increasingly profound crisis of clarity. In response to the limitations of static compliance-based approaches, this study proposes the Adaptive and Layered AI Governance Model (ALAIGoM), a model of adaptive and layered AI governance that integrates operationalized ethical principles, multi-level governance structures, and a continuous ethics life cycle to enable context-sensitive, adaptive oversight. The model is grounded in proactive trust-building, human-centered design, and mechanisms that ensure acceptability. Overall, this study offers a coherent roadmap for reconfiguring AI ethics from a mere normative checklist into an embedded, adaptive, and responsive governance process.

Artificial Intelligence (AI) is increasingly being adopted across many industries including healthcare. This has brought forth the development of many new independent ethical frameworks for responsible use of AI within institutions and companies. Risks associated with the application of AI in healthcare have high stakes for patients. Further, the existence of multiple frameworks may exacerbate these risks due to potential differences in interpretation and prioritization in said frameworks. Resolving these risks requires an ethical framework that is both broadly adopted in healthcare settings and applicable to AI. Here, we examined whether a framework consisting of the 4 well-established principles of biomedical ethics (i.e., Beneficence, Non-Maleficence, Respect for Autonomy, and Justice) can serve as a foundation for an ethical framework for AI in healthcare. To this end, we conducted a scoping review of 227 peer-reviewed papers using semi-inductive thematic analyses to categorize patient-related ethical issues in healthcare AI under these 4 principles of biomedical ethics. We found that these principles, which are already widely adopted in healthcare settings, were comprehensively and internationally applicable to ethical considerations concerning use of AI in healthcare. The existing four principles of biomedical ethics can provide a foundational ethical framework for applying AI in healthcare, grounding other Responsible AI frameworks, and can act as a basis for AI governance and policy in healthcare.

The rapid advancement of artificial intelligence (AI) has revolutionized the accounting profession, automating tasks, identifying patterns, and improving accuracy. However, the increasing reliance on AI raises ethical concerns regarding privacy, bias, transparency, and accountability. This research paper delves into the ethical considerations of AI implementation in accounting practices.Thepaper begins by examining the potential benefits of AI in accounting, highlighting its ability to streamline operations, enhance efficiency, and reduce errors. However, it also acknowledges the ethical risks associated with AI, including data privacy breaches, biased decision-making, lack of transparency, and accountability issues.The paper proposes a framework for responsible AI implementation in accounting to address these ethical concerns. The framework emphasizes establishing clear ethical guidelines,ensuring data privacy and security, mitigating AI algorithms' bias, promoting AI decisionmaking transparency, and establishing accountability mechanisms.The paper further explores the role of accountants in addressing AI ethics. Accountants are responsible for upholding ethical standards and ensuring that AI systems are used responsibly and ethically. They must be aware of the ethical implications of AI and have the knowledge and skills to mitigate ethical risks.In conclusion, the paper emphasizes the need for a proactive approach to AI ethics in accounting. By establishing clear ethical guidelines, promoting responsible AI implementation, and empowering accountants with ethical knowledge and skills, the accounting profession can harness the potential of AI while upholding ethical principles and safeguarding public trust.

Artificial intelligence (AI) ethics is undergoing a practical shift towards putting principles into design practices in developing responsible AI. While this practical turn is essential, this paper highlights its potential risk of overly focusing on addressing issues at the level of individual artifacts, which can neglect more profound structural challenges and the need for significant systemic change. Such oversight makes AI ethics lose its strength in addressing some hidden, long-term harms within broader contexts. In this paper, we propose that the reflection on structural issues should be an integral part of AI ethics. To achieve this, we develop a multi-level framework to analyze socio-ethical issues of AI at both the artifact and broader structural levels. This framework can serve as a potentially transformative approach to uncover some unspoken assumptions in current AI ethics discourses and expose some blind spots in AI guidelines, policies, and regulations. Our paper paves the way to develop a practical approach that can effectively integrate this multi-level framework into real-world AI design and policymaking, ultimately bringing about transformative change.

Emerging technologies, including artificial intelligence, biotechnology, and data science, have significantly transformed human life, offered unprecedented benefits while simultaneously raised profound ethical concerns. This paper examines the ethical dimensions of emerging technologies, focusing on key dilemmas such as privacy, data security, algorithmic bias, genetic engineering, and environmental sustainability. It examines the historical evolution of bioethics, the role of stakeholders in ethical decision-making, and the need for regulatory frameworks that ensure responsible innovation. By evaluating case studies and ethical principles, this paper highlights the challenges of balancing technological advancements with moral considerations. The findings suggest that interdisciplinary collaboration, ethical foresight, and regulatory adaptations are crucial for ensuring the responsible development and deployment of emerging technologies. Keywords: Bioethics, Emerging Technologies, Artificial Intelligence Ethics, Data Privacy and Security, Genetic Engineering, Responsible Innovation, Environmental Sustainability.

As quantum computing development advances closer toward achieving fault-tolerant error-corrected realisation, debates on applications, impacts, risks, and benefits of quantum computing are timely and due. While there is awareness of the potential power and complexity of quantum computers, there has been relatively little attention on the social impacts and ethical implications of this technology. In grappling with the social impacts of quantum computing, some stakeholders have turned to applied ethics, specifically (classical) computer and Artificial Intelligence (AI) ethics, for guidance. While computer and AI ethics are useful starting points for evaluating the ethical issues posed by quantum computing, uncritically applying existing sets of ethical principles risks overlooking how quantum computing differs from these technologies. We argue that borrowing of ethical principles and guidelines from AI and computing is inappropriate for several reasons: (1) quantum computing, classical computing, and AI are different technologies with significant material differences; (2) unlike AI and classical computers which have become established, quantum computing is an emerging technology, which has implications on the levels of accessibility and immediate impact in society; and (3) there are significant differences in the way AI and quantum computing have been developed and by whom. We also briefly summarise some of the unique risks and societ al impacts posed by quantum computing. We posit that these reasons support the argument for a new sub-field of quantum computing ethics, which would allow a relevant scholarship to develop and provide guidance as quantum computing technology continues to mature.

Although numerous ethical principles and guidelines have been proposed to guide the development of artificial intelligence (AI) systems, it has proven difficult to translate these principles into actionable practices beyond mere adherence to ethical ideas. This is particularly challenging in the context of AI systems for healthcare, which requires balancing the potential benefits of the solution against the risks to patients and the wider community, including minorities and underserved populations. To address this challenge, we propose a shift from one-size-fits-all ethical principles to contextualized case-based ethical frameworks. This study uses an AI-enabled mHealth application as a case study. Our framework is built on existing ethical guidelines and principles, including the AI4People framework, the EU High-Level Expert Group on trustworthy AI, and wider human rights considerations. Additionally, we incorporate relational perspectives to address human value concerns and moral tensions between individual rights and public health. Our approach is based on ”ethics by design,” where ethical principles are integrated throughout the entire AI development pipeline, ensuring that ethical considerations are not an afterthought but implemented from the beginning. For our case study, we identified 7 ethical principles: fairness, agility, precision, safeguarding humanity, respect for others, trust and accountability, and robustness and reproducibility. We believe that the best way to mitigate and address ethical consequences is by implementing ethical principles in the software development processes that developers commonly use. Finally, we provide examples of how our case-based framework can be applied in practice, using examples of AI-driven mobile applications in healthcare.

This article dives into the ethical complexities of Artificial Intelligence (AI). It starts by laying the groundwork, defining both ethics and morality, and emphasizing how ethical principles can evolve over time.Next, it explores "applied ethics," which examines real-world dilemmas like genetic engineering, AI's impact on jobs, and environmental concerns. From there, it shifts its focus specifically to AI ethics, differentiating between two key aspects the Ethics of AI where broader framework considers ethical issues surrounding AI development, encompassing concerns like transparency, data security, privacy, and accountability and in Ethical AI where the concept focuses on building AI systems with the ability to make ethical choices. It emphasizes aligning these systems with ethical values, minimizing bias, and fostering user trust. The article delves deeper into specific ethical concerns in the AI landscape, such as transparency, data privacy, and issues of autonomy, intentionality, and responsibility. It explores their relevance and the challenges they pose for AI development. Furthermore, it examines the social implications of these ethical concerns. Key areas examined include automation and job displacement, equitable access to technology, and the potential impact of AI on democratic principles and civil liberties. The article highlights the potential consequences of AI-driven automation on employment, the need for inclusive digital access, and the importance of ethical considerations in governance and citizen rights amidst the growing influence of AI. The article serves as a comprehensive guide to understanding the ethical dimensions of AI. It covers fundamental ethical concepts, specific AI ethics considerations, the importance of building ethical AI systems, and the challenges and societal impacts of AI development. It's a valuable resource for anyone seeking to understand the ethical landscape surrounding Artificial Intelligence.

Artificial intelligence (AI) has transformed many aspects of modern life and continues to do so at an accelerating pace. AI is being deployed across diverse sectors including autonomous driving, healthcare, media, finance, industrial robotics, and online services. While this broad integration into the economy and society has improved efficiency and generated substantial benefits, it has also altered social structures and raised significant ethical concerns. Issues such as privacy breaches, algorithmic discrimination, workforce displacement, and security risks associated with AI systems have become matters of public, organisational, and governmental concern. Accordingly, AI ethics—the study of the ethical issues surrounding AI—has emerged as a critical research area. This article provides a systematic overview of the field by summarising and analysing the ethical risks and issues raised by AI, reviewing ethical guidelines and principles published by major organisations, examining approaches to address these issues, and discussing methods to evaluate the ethics of AI systems. Additionally, the article discusses challenges in implementing ethics in AI and outlines future research perspectives. This work aims to offer a clear, accessible overview of AI ethics for researchers, practitioners, and newcomers to the field.

In response to the COVID-19 crisis, Artificial Intelligence (AI) has been applied to a range of applications in healthcare and public health such as case identification or monitoring of the population. The urgency of the situation should not be to the detriment of considering the ethical implications of such apps. Implementing ethics in medical AI is a complex issue calling for a systems thinking approach engaging diverse representatives of the stakeholders in a consultative process. The participatory engagement aims to gather the different perspectives of the stakeholders about the app in a transparent and inclusive way. In this study, we engaged a group of clinicians, patients, and AI developers in conversations about a fictitious app which was an aggregate of actual COVID-19 apps. The app featured a COVID-19 symptoms monitoring function for both the patient and the clinician, as well as infection clusters tracking for health agencies. Anchored in Soft Systems Methodology and Critical Systems Thinking, participants were asked to map the flow of knowledge between the clinician, the patient, and the AI app system and answer questions about the ethical boundaries of the system. Because data and information are the resource and the product of the AI app, understanding the nature of the information and knowledge exchanged between the different agents of the system can reveal ethical issues. In this study, not only the output of the participatory process was analysed, but the process of the stakeholders’ engagement itself was studied as well. To establish a strong foundation for the implementation of ethics in the AI app, the conversations among stakeholders need to be inclusive, respectful and allow for free and candid dialogues ensuring that the process is transparent for which a systemic intervention is well suited.

Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation

With ChatGPT’s rapid adoption, concerns regarding generative artificial intelligence (AI) have shifted from theoretical to practical. Drawing upon the “algorithmic imaginary” framework from critical algorithm studies and the anthropological concept of “ordinary ethics,” we analyzed Twitter discourse during ChatGPT’s initial deployment, examining 368,359 tweets. Our analysis identified five topics reflecting functional and critical aspects of ChatGPT. We specifically point to two topics with a critical perspective: “Ethics” and “Concerns.” The first aligns with scholarly discussions in AI ethics on fairness and transparency, while the second focuses on ChatGPT’s generative capabilities. This highlights an emerging trend: While the academic discussion on AI ethics has gained popularity, especially in scrutinizing ChatGPT, the conversation is now expanding to more nuanced ethical deliberations. We analyzed the posts’ engagement and sentiment over time, demonstrating the AI ethics community’s influence in addressing the potential and harms of generative AI systems.