This article explores the cooperation of government and the private sector to tackle the ethical dimension of artificial intelligence. The argument draws on the institutionalist approach in philosophy and business ethics defending a ‘division of moral labor’ between governments and the private sector. The goal and main contribution of this article is to explain how this approach can provide ethical guidelines to the AI industry and to highlight the limits of self-regulation. In what follows, I discuss three institutionalist claims. (...) First, principles of AI ethics should be validated through legitimate democratic processes. Second, compliance with these principles should be secured in a stable way. Third, their implementation in practice should be as efficient as possible. If we accept these claims, there are good reasons to conclude that, in many cases, governments implementing hard regulation are in principle the best instruments to secure an ethical development of AI systems. Where adequate regulation exists, firms should respect the law. But when regulation does not yet exist, helping governments build adequate regulation should be businesses’ ethical priority, not self-regulation. (shrink)

. The management of ethics within organisations typically occurs within a problem-solving frame of reference. This often results in a reactive, problem-based and externally induced approach to managing ethics. Although basing ethics management interventions on dealing with and preventing current and possible future unethical behaviour are often effective in that it ensures compliance with rules and regulations, the approach is not necessarily conducive to the creation of sustained ethical cultures. Nor does the approach afford (mainly internal) (...) stakeholders the opportunity to be co-designers of the organisations ethical future. The aim of this paper is to present Appreciative Inquiry (AI) as an alternative approach for developing a shared meaning of ethics within an organisation with a view to embrace and entrench ethics, thereby creating a foundation for the development of an ethical cul- ture over time. A descriptive case study based on an application of AI is used to illustrate the utility of AI as a way of thinking and doing to precede and complement problem-based ethics management systems and interventions. (shrink)

Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the (...) allocation outcomes? If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance by k% of employers can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; (4) partial compliance based on local parity measures can induce extreme segregation. Finally, we discuss implications for auditors and insights concerning the design of regulatory frameworks. (shrink)

Empathy is a specific moral aspect of human behavior. The global workplace, and thereby a consideration of employee stakeholders, includes unique behavioral and ethical considerations, including a consideration of human empathy. Further, the human aspects of workplaces are within the domain of human resources and managerial oversight in business organizations. As such, human emotions and interactions are complicated by daily work related expectations, employee/employer interactions and work practices, and the outcomes of employees’ work routines. Business ethics, human resources, and (...) risk management practices are endemic aspects within workplaces. Increasingly, the understanding of models of AI-reliant business practices underscores the need for the consideration of the ethical aspects of AI impacts on employees in the workplace. This paper explores a systematic ethical lens of the opportunities and the risks of AI ideation, development, and deployment in business-employee relations practices beyond a compliance mindset, and that introduces a further set of workplace considerations. Empathy is concerned with human intentions. As such, attributive ethical indications of the role of AI in the workplace and its impacts on employees is necessary. Moreover, this paper uses a cognitive lens of empathy and focuses on artificial morality related to the ethical concerns, implications, and practices of AI development, deployment, and workplace practices that may impact employees in a variety of business aspects. (shrink)

Artificial intelligence (AI) governance and auditing promise to bridge the gap between AI ethics principles and the responsible use of AI systems, but they require assessment mechanisms and metrics. Effective AI governance is not only about legal compliance; organizations can strive to go beyond legal requirements by proactively considering the risks inherent in their AI systems. In the past decade, investors have become increasingly active in advancing corporate social responsibility and sustainability practices. Including nonfinancial information related to environmental, (...) social, and governance (ESG) issues in investment analyses has become mainstream practice among investors. However, the AI auditing literature is mostly silent on the role of investors. The current study addresses two research questions: (1) how companies’ responsible use of AI is included in ESG investment analyses and (2) what connections can be found between principles of responsible AI and ESG ranking criteria. We conducted a series of expert interviews and analyzed the data using thematic analysis. Awareness of AI issues, measuring AI impacts, and governing AI processes emerged as the three main themes in the analysis. The findings indicate that AI is still a relatively unknown topic for investors, and taking the responsible use of AI into account in ESG analyses is not an established practice. However, AI is recognized as a potentially material issue for various industries and companies, indicating that its incorporation into ESG evaluations may be justified. There is a need for standardized metrics for AI responsibility, while critical bottlenecks and asymmetrical knowledge relations must be tackled. (shrink)

BackgroundHealth data-driven activities have become central in diverse fields (research, AI development, wearables, etc.), and new ethical challenges have arisen with regards to privacy, integrity, and appropriateness of use. To ensure the protection of individuals’ fundamental rights and freedoms in a changing environment, including their right to the protection of personal data, we aim to identify the ethical approaches adopted by scientists during intensive data exploitation when collecting, using, or sharing peoples’ health data.MethodsTwelve scientists who were collecting, using, or sharing (...) health data in different contexts in Sweden, were interviewed. We used systematic expert interviews to access these scientists’ specialist knowledge, and analysed the interviews with thematic analysis. Phrases, sentences, or paragraphs through which ethical values and norms were expressed, were identified and coded. Codes that reflected similar concepts were grouped, subcategories were formulated, and categories were connected to traditional ethical approaches.ResultsThrough several examples, the respondents expressed four different ethical approaches, which formed the main conceptual categories: consideration of consequences, respect for rights, procedural compliance, and being professional.ConclusionsTo a large extent, the scientists’ ethical approaches were consistent with ethical and legal principles. Data sharing was considered important and worth pursuing, even though it is difficult. An awareness of the complex issues involved in data sharing was reflected from different perspectives, and the respondents commonly perceived a general lack of practical procedures that would by default ensure ethical and legally compliant data collection and sharing. We suggest that it is an opportune time to move on from policy discussions to practical technological ethics-by-design solutions that integrate these principles into practice. (shrink)

The ethics of artificial intelligence is an upcoming field of research that deals with the ethical assessment of emerging AI applications and addresses the new kinds of moral questions that the advent of AI raises. The argument presented in this article is that, even though there exist different approaches and subfields within the ethics of AI, the field resembles a critical theory. Just like a critical theory, the ethics of AI aims to diagnose as well as change (...) society and is fundamentally concerned with human emancipation and empowerment. This is shown through a power analysis that defines the most commonly addressed ethical principles and topics within the field of AI ethics as either to do with relational power or with dispositional power. Moreover, it is concluded that recognizing AI ethics as a critical theory and borrowing insights from the tradition of critical theory can help the field forward. (shrink)

The ethics of artificial intelligence, or AI ethics, is a rapidly growing field, and rightly so. While the range of issues and groups of stakeholders concerned by the field of AI ethics is expanding, with speculation about whether it extends even to the machines themselves, there is a group of sentient beings who are also affected by AI, but are rarely mentioned within the field of AI ethics—the nonhuman animals. This paper seeks to explore the kinds (...) of impact AI has on nonhuman animals, the severity of these impacts, and their moral implications. We hope that this paper will facilitate the development of a new field of philosophical and technical research regarding the impacts of AI on animals, namely, the ethics of AI as it affects nonhuman animals. (shrink)

This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous (...) improvement in AI systems. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance. The paper concludes with strategies for resolving conflicts between ethical directives, underscoring the present and future need for robust, nuanced and context-aware AI systems. (shrink)

Over the last few years, various public, private, and NGO entities have adopted a staggering number of non-binding ethical codes to guide the development of artificial intelligence. However, this seemingly failed to drive better ethical practices within AI organizations. In light of this observation, this paper aims to reevaluate the roles the ethics of AI can play to have a meaningful impact on the development and implementation of AI systems. In doing so, we challenge the notion that AI (...) class='Hi'>ethics should focus primarily on instilling ethical principles in practitioners within AI organizations, as well as the claim that AI ethics can only lead to ethics washing. We propose a two-pronged institutionalist approach to AI ethics, focusing on shaping organizational decision-making processes and emphasizing the necessity of binding legal regulations. First, we argue that AI ethics should give priority to institutional design over the internalization of ethical principles by individual practitioners. We then contend that legally binding rules are needed to this end, both as a motivation for organizations and to contribute to the semantic determination of high-level ethical principles. We then show that promising proposals to operationalize ethical principles require the backing of binding legal norms to be effective. We conclude by highlighting the potential of AI ethics to contribute meaningfully to legislative innovation in AI governance. (shrink)

Ethics has powerful teeth, but these are barely being used in the ethics of AI today – it is no wonder the ethics of AI is then blamed for having no teeth. This article argues that ‘ethics’ in the current AI ethics field is largely ineffective, trapped in an ‘ethical principles’ approach and as such particularly prone to manipulation, especially by industry actors. Using ethics as a substitute for law risks its abuse and misuse. (...) This significantly limits what ethics can achieve and is a great loss to the AI field and its impacts on individuals and society. This article discusses these risks and then highlights the teeth of ethics and the essential value they can – and should – bring to AI ethics now. (shrink)

Much of the current work on AI ethics has lost its connection to the real-world impact by making AI ethics operable. There exist significant limitations of hyper-focusing on the identification of abstract ethical principles, lacking effective collaboration among stakeholders, and lacking the communication of ethical principles to real-world applications. This position paper presents challenges in making AI ethics operable and highlights key obstacles to AI ethics impact. A preliminary practice example is provided to initiate practical implementations (...) of AI ethics. We aim to inspire discussions on making AI ethics operable and focus on its impact on real-world applications. (shrink)

Many popular artificial intelligence (AI) ethics frameworks center the principle of autonomy as necessary in order to mitigate the harms that might result from the use of AI within society. These harms often disproportionately affect the most marginalized within society. In this paper, we argue that the principle of autonomy, as currently formalized in AI ethics, is itself flawed, as it expresses only a mainstream mainly liberal notion of autonomy as rational self-determination, derived from Western traditional philosophy. In (...) particular, we claim that the adherence to such principle, as currently formalized, does not only fail to address many ways in which people’s autonomy can be violated, but also to grasp a broader range of AI-empowered harms profoundly tied to the legacy of colonization, and which particularly affect the already marginalized and most vulnerable on a global scale. To counter such a phenomenon, we advocate for the need of a relational turn in AI ethics, starting from a relational rethinking of the AI ethics principle of autonomy that we propose by drawing on theories on relational autonomy developed both in moral philosophy and Ubuntu ethics. (shrink)

This article provides a methodology for the interpretation of AI ethics principles to specify ethical criteria for the development and deployment of AI systems in high-risk domains. The methodology consists of a three-step process deployed by an independent, multi-stakeholder ethics board to: (1) identify the appropriate level of abstraction for modelling the AI lifecycle; (2) interpret prescribed principles to extract specific requirements to be met at each step of the AI lifecycle; and (3) define the criteria to inform (...) purpose- and context-specific balancing of the principles. The methodology presented in this article is designed to be agile, adaptable, and replicable, and when used as part of a pro-ethical institutional culture, will help to foster the ethical design, development, and deployment of AI systems. The application of the methodology is illustrated through reference to the UK Ministry of Defence AI ethics principles. (shrink)

AI ethics is proposed, by the Big Tech companies which lead AI research and development, as the cure for diverse social problems posed by the commercialization of data-intensive technologies. It aims to reconcile capitalist AI production with ethics. However, AI ethics is itself now the subject of wide criticism; most notably, it is accused of being no more than “ethics washing” a cynical means of dissimulation for Big Tech, while it continues its business operations unchanged. This (...) paper aims to critically assess, and go beyond the ethics washing thesis. I argue that AI ethics is indeed ethics washing, but not only that. It has a more significant economic function for Big Tech. To make this argument I draw on the theory of intellectual monopoly capital. I argue that ethics washing is better understood as a subordinated innovation network: a dispersed network of contributors beyond Big Tech’s formal employment whose research is indirectly planned by Big Tech, which also appropriates its results. These results are not intended to render AI more ethical, but rather to advance the business processes of data-intensive capital. Because the parameters of AI ethics are indirectly set in advance by Big tech, the ostensible goal that AI ethics sets for itself—to resolve the contradiction between business and ethics—is in fact insoluble. I demonstrate this via an analysis of the latest trend in AI ethics: the operationalization of ethical principles. (shrink)

This paper puts forth Central Asian AI ethics principles and proposes a layered strategy tailored for the development of ethical principles in the field of artificial intelligence (AI) in Central Asian countries. This approach includes the customization of AI ethics principles to resonate with local nuances, the formulation of national and regional-level AI ethics principles, and the implementation of sector-specific principles. While countering the narrative of ineffectiveness of the AI ethics principles, this paper underscores the importance (...) of stakeholder collaboration, provides a comprehensive framework, and emphasizes the need for responsible AI practices. By adopting this approach, Central Asian region can contribute towards the regional integration and global discourse on AI ethics while promoting the responsible use of AI technology in their respective countries. (shrink)

Artificial intelligence plays an important role in current discussions on information and communication technologies and new modes of algorithmic governance. It is an unavoidable dimension of what social mediations and modes of reproduction of our information societies will be in the future. While several works in artificial intelligence ethics address ethical issues specific to certain areas of expertise, these ethical reflections often remain confined to narrow areas of application, without considering the global ethical issues in which they are embedded. (...) We, therefore, propose to clarify the main approaches to AIE, their philosophical assumptions and the specific characteristics of each one of them, to identify the most promising approach to develop an ethical reflection on the deployment of AI in our societies, which is the one based on information ethics as proposed by Luciano Floridi. We will identify the most important features of that approach to highlight areas that need further investigation. (shrink)

The ethical Principle of Proportionality requires combatants not to cause collateral harm excessive in comparison to the anticipated military advantage of an attack. This principle is considered a major (and perhaps insurmountable) obstacle to ethical use of autonomous weapon systems (AWS). This article reviews three possible solutions to the problem of achieving Proportionality compliance in AWS. In doing so, I describe and discuss the three components Proportionality judgments, namely collateral damage estimation, assessment of anticipated military advantage, and judgment of (...) “excessiveness”. Some possible approaches to Proportionality compliance are then presented, such as restricting AWS operations to environments lacking civilian presence, using AWS in targeted strikes in which proportionality judgments are pre-made by human commanders, and a ‘price tag’ approach of pre-assigning acceptable collateral damage values to military hardware in conventional attritional warfare. The article argues that application of these three compliance methods would result in AWS’ achieving acceptable Proportionality compliance levels in many combat environments and scenarios, allowing AWS to perform most key tasks in conventional warfare. (shrink)

Background Head nurses have duties in providing nursing care and ethical supervision to the nurses in the unit. Compliance with the ethical competence framework for head nurses is essential in fostering an ethical climate in the organization. Objective The objective of this research is to study the head nurses’ compliance with the ethical competence framework by the Thailand Nursing and Midwifery Council (TNMC). Methods The study is a qualitative research, using in-depth interviews conducted among 20 head nurses practicing (...) in a super tertiary hospital in Bangkok, Thailand. The sample was selected using a criterion sampling method. The head nurses’ ethical experience relating to work practice was analyzed by thematic analysis. Ethical considerations The ethical approval of this study was obtained from the Committee for Research Ethics (Social Sciences), Mahidol University, and from the Institutional Review Board of the hospital in which the study was conducted. Results From the thematic analysis of the ethical experience practiced by the head nurses, 8 themes and 16 sub-themes were categorized across 5 ethical competencies based on the ethical competence framework by the TNMC. It was found that all the identified themes and sub-themes conformed to the ethical competence framework, and demonstrated the head nurses’ compliance with the framework. Discussion Head nurses’ ethical experience in practice conforms to the ethical competence framework by the TNMC. The framework that is based on the Code of conduct and the nursing culture that places the importance of the seniority system may facilitate the compliance with the ethical competence framework. Conclusion Head nurses demonstrated compliance with the ethical competence framework by the TNMC. Nevertheless, improvements could be done to the framework by adding details such as more examples of ethical decision-making scenarios, personnel’s rights, and social media usage guidelines. (shrink)

For implementing ethics in AI technology, there are at least two major ethical challenges. First, there are various competing AI ethics guidelines and consequently there is a need for a systematic overview of the relevant values that should be considered. Second, if the relevant values have been identified, there is a need for an indicator system that helps assessing if certain design features are positively or negatively affecting their implementation. This indicator system will vary with regard to specific (...) forms of AI technology. An adequate indicator system for the ethical development of recommendation algorithms, for example, will diverge considerably from another for autonomous road vehicles, although both are based on shared values. In this contribution, we propose solutions to both challenges with regard to the special case of the development of an AI-assisted rescue robot. (shrink)

There is now a robust literature on the ethics of artificial intelligence (AI) that pertains largely to non-military issues – issues of, among other things, bias, fairness, and unintended consequences. There is less published work, however, on how these lessons from industry and academia might inform the ethics of AI in the military context. In this article, I take small steps to demonstrate the ways in which the field of AI ethics might be relevant to military applications. (...) Ultimately, I argue that even though the outcomes of AI failures might look very different in the non-military context versus in the military context, the causes of those failures can be very similar; and addressing those potential causes of AI failures in the development of military AI systems will result in fewer ethical failures after deployment. (shrink)

There is now a robust literature on the ethics of artificial intelligence (AI) that pertains largely to non-military issues – issues of, among other things, bias, fairness, and unintended consequences. There is less published work, however, on how these lessons from industry and academia might inform the ethics of AI in the military context. In this article, I take small steps to demonstrate the ways in which the field of AI ethics might be relevant to military applications. (...) Ultimately, I argue that even though the outcomes of AI failures might look very different in the non-military context versus in the military context, the causes of those failures can be very similar; and addressing those potential causes of AI failures in the development of military AI systems will result in fewer ethical failures after deployment. (shrink)

Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS (Ethical Technology and Holistic Oversight System) framework—a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry for AI (...) agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring through tools like soulbound tokens and zero-knowledge proofs. Furthermore, the framework incorporates decentralized justice systems for transparent dispute resolution and introduces AI-specific legal entities to manage limited liability, supported by mandatory insurance to ensure financial accountability and incentivize ethical design. By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance. This innovative framework offers a scalable and inclusive strategy for regulating AI agents, balancing innovation with ethical responsibility to meet the demands of an AI-driven future. (shrink)

Theories of business ethics or corporate responsibility tend to focus on justifying obligations that go above and beyond what is required by law. This article examines the curious fact that most business ethics scholars use concepts, principles, and normative methods for identifying and justifying these beyond-compliance obligations that are very different from the ones that are used to set the levels of regulations themselves. Its modest proposal—a plea for a research agenda, really—is that we could reduce this (...) normative asymmetry by borrowing from the normative framework of “regulation” to identify and justify an important range of beyond-compliance obligations. In short, we might think of “self-regulation” as a language and a normative framework with some distinct advantages over other frameworks like stakeholder theory, corporate social responsibility, corporate citizenship, and the like. These other frameworks have been under attack in the business ethics literature of late, primarily for their vagueness and their disappointing inability to distinguish clearly between genuine beyond-compliance moral obligations, on the one hand, and charitable acts that are laudable but not morally obligatory, on the other. (shrink)

A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has far-reaching moral implications (...) for AI development. Although the most obvious way to avoid the tension between alignment and ethical treatment would be to avoid creating AI systems that merit moral consideration, this option may be unrealistic and is perhaps fleeting. So, we conclude by offering some suggestions for other ways of mitigating mistreatment risks associated with alignment. (shrink)

The AI revolution provides a neat illustration of C.P. Snow's ideas regarding "the two cultures" and a timely opportunity to reflect on why mutual suspicion persists between those in the natural sciences, on the one hand, and the humanities (and to an extent the social sciences), on the other.

This paper introduces the Spider Vision Framework, a biomimetic approach to AI governance inspired by the dual visual systems of spiders. By integrating focused oversight (technical detail) with systemic awareness (societal context) and grounding both in virtue ethics—particularly prudence, justice, and adaptability—the framework addresses immediate technical risks while accounting for long-term societal implications. Comparative analyses with consequentialist and deontological models underscore virtue ethics’ emphasis on moral character, and the paper proposes pilot studies for empirical validation in healthcare AI (...) and autonomous vehicles. This integrated approach aims to foster an adaptive, ethically robust governance model, balancing precision and breadth to better align AI with the common good. Initial findings suggest a significant improvement in ethical compliance and stakeholder engagement when dual-focus oversight is integrated into existing AI systems. The framework recommends targeted pilot implementations across diverse regulatory environments to ensure robust, context-specific outcomes. (shrink)

The Yearbook 2021 for Global Ethics, Compliance & Integrity offers an upto- date overview of the recent and most significant developments in the interdisciplinary area of organizational Ethics, Compliance & Integrity Management. The 2021 Yearbook focuses on (but is not limited to) integrity and ethics and consists of 40 highly valuable articles submitted by 55 experts. The authors include excellent ethics, compliance and integrity professionals, scholars and advisors from 20 different countries. As conceived, (...) the publication offers exclusive insights both from practical and theoretical perspectives. This year's edition consists of seven carefully arranged chapters dealing with governance and compliance management, integrity management, organizational ethics and culture, anti-bribery management, whistleblowing, the challenges of digitalization, and - last but not least - corporate incentives and sanctions. In each chapter, the yearbook provides a comprehensive range of views and expertise regarding Ethics, Compliance and Integrity in all kind of organizations. (shrink)

Why do people comply with coronavirus disease 2019 public health guidance? This study considers cultural-psychological foundations of variation in beliefs about motivations for such compliance. Specifically, we focused on beliefs about two sources of prosocial motivation: desire to protect others and obligation to society. Across two studies, we observed that the relative emphasis on the desire to protect others as an explanation for compliance was greater in the United States settings associated with cultural ecologies of abstracted independence than (...) in Chinese settings associated with cultural ecologies of embedded interdependence. We observed these patterns for explanations of psychological experience of both others and self, and for compliance with mandates for both social distancing and face masks. Discussion of results considers both practical implications for motivating compliance with public health guidance and theoretical implications for denaturalizing prevailing accounts of prosocial motivation. (shrink)

The search for ethical guidance in the development of artificial intelligence (AI) systems, especially in healthcare and decision support, remains a crucial effort. So far, principles usually serve as the main reference points to achieve ethically correct implementations. Based on reviewing classical criticism of principle-based ethics and taking into account the severity and potentially life-changing relevance of decisions assisted by AI-driven systems, we argue for strengthening a complementary perspective that focuses on the life-world as ensembles of practices which shape (...) people’s lives. This perspective focuses on the notion of ethical judgment sensitive to life forms, arguing that principles alone do not guarantee ethicality in a moral world that is rather a joint construction of reality than a matter of mere control. We conclude that it is essential to support and supplement the implementation of moral principles in the development of AI systems for decision-making in healthcare by recognizing the normative relevance of life forms and practices in ethical judgment. (shrink)

Artificial intelligence (AI) ethics is proposed as an emerging and interdisciplinary field concerned with addressing the ethical issues of AI, such as the issue of moral decision-making. The conflict between our intuitive moral judgments constitutes an inevitable obstacle to decision-making in AI ethics. This article outlines the Moral Relevance Approach, which could provide a considerable moral foundation for AI ethics. Taking moral relevance as the precondition of the consequentialist principles, the Moral Relevance Approach aims to plausibly consider (...) individual moral claims. It is not only the common ethical target shaping our moral consensus but also the inherent moral ability connecting others with us. (shrink)

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

The Statements on Responsibilities in Tax Practice (SRTPs) provide guidance to the CPA when making decisions in tax practice. Many of these decisions are ethical in nature and have implications for tax compliance. In this study, a survey methodology is used to test whether the SRTPs affect decisions that CPAs make. The findings suggest that a clear majority of CPAs follow the SRTPs when making ethical decisions relating to tax return preparation and that CPAs follow the SRTPs more often (...) than unlicensed preparers on half the issues tested. However, a statistically significant number of CPAs do not follow the SRTPs and, CPAs do not follow the SRTPs any more often than unlicensed tax preparers on three issues. (shrink)

The effective implementation of regulation in organizations is an ongoing concern for both research and practice, in order to avoid deviant behavior and its consequences. However, the way compliance with regulations is actually enacted or “performed” within organizations instead of merely executed, remains largely under-characterized. Evidence from an ethnographic study in the compliance unit of a French investment bank allows us to develop a detailed practice approach to how regulation is actually implemented in firms. We characterize the work (...) accomplished by compliance analysts who are in fact, “curving” the script of regulation within what we conceptualize as a “comfort zone”. Beyond agency, ethics appears as a key element in linking the “letter of the law”, which serves as a referential anchor to guide action, with the complex nature of specific situations. We analyze the way individuals and compliance teams cope with, interpret, struggle and in fine, perform regulation within this comfort zone. A particular interest is thus given to the work of embedded ethics in this process, as an enabler to partly recouple compliance with the regulated activity. We find that blind execution is not only impossible, but also devoid of meaning both from regulatory, risk management, and business perspectives in organizations. We highlight and characterize a hermeneutic dimension to this work, essential to effectively perform regulation in complex environments, and we suggest some directions for further research. (shrink)

From machine learning and computer vision to robotics and natural language processing, the application of data science and artificial intelligence is expected to transform health care (Ce...

Artificial intelligence (AI) is booming, and AI ethics is booming with it. Yet there is surprisingly little attention paid to what the discipline of AI ethics is and what it ought to be. This paper offers an ameliorative definition of AI ethics to fill this gap. We introduce and defend an original distinction between novel and applied research questions. A research question should count as AI ethics if and only if (i) it is novel or (ii) (...) it is applied and has gained new importance through the development of AI. We argue that a lack of such a definition contributes to six disciplinary problems: ethics washing and lobbying, limited applicability, dilution of the field, conceptual bloating, costs of AI ethics, and an internal dispute. Based on our definition, we construct a methodological framework for AI ethics and show how it helps address these problems. (shrink)

The rapid growth of AI service businesses presents significant legal and financial challenges, particularly concerning liability protection, regulatory compliance, and risk mitigation. A robust legal framework is essential as AI enterprises navigate issues like algorithmic bias, misinformation, privacy violations, and regulatory inconsistencies. This paper examines how business structures—Godo-Kaisha in Japan and Limited Liability Companies (LLCs) and Series LLCs in the U.S.—shape liability exposure and corporate governance in AI enterprises. A key contribution of this study is its _focus on legal (...) structures as a foundation for AI’s social implementation_. Granting legal status to AI service businesses enables the assignment of rights and obligations, ensuring effective risk allocation, governance, and regulatory compliance. This highlights the interaction between business law and AI governance in fostering responsible innovation. Through comparative legal analysis, this paper explores how liability structures influence AI governance, corporate accountability, and ethical compliance. It examines the EU AI Act’s dual-axis risk classification and emerging governance models such as the OECD AI Principles and the G7 Hiroshima AI Process, with Series LLCs proposed as a novel approach to fluctuating AI risks. This paper addresses three key research questions: (1) Impact of AI risks on liability exposure for business participants. (2) Comparative analysis of liability protection in Japan and the U.S. (3) Role of Series LLCs and other legal structures in enhancing AI business. By integrating AI governance with corporate law, this study provides a theoretical foundation for aligning liability protection with AI risk landscapes, offering insights for lawmakers, policymakers, and AI business participants navigating global AI regulations. (shrink)

This study addresses the current lack of empirical data on the experiences and attitudes of clinical research professionals towards AI-powered clinical trial management tools. Clinical research professionals affiliated with various Swiss and international clinical research networks were invited to participate in an online survey. The survey focused on nine use cases of AI-powered clinical trial management tools. Participants were asked to share their ethical considerations, and their experiences were assessed at both the individual and institutional levels. Answers from 110 participants, (...) primarily from Swiss academic institutions, were included in the data analysis. While participants generally held positive attitudes, mixed views were also common. Governmental attitudes were widely perceived as unclear or cautious, with the associated regulatory opacity possibly contributing to the limited adoption of AI in trial management observed in this study. In addition to a clear AI governance, ethical compliance and training emerged as crucial aspects. There was also broad consensus on the necessity of informing patients about tools’ uses and of ensuring an independent certification of the respective tools. The widespread adoption of AI-powered clinical trial management tools remains distant, entangled in a net of complexities that require further empirical investigation. Ethical concerns appear to play a crucial role, driven by distrust and the absence of a clear regulatory framework. Urgent actions are needed at both institutional and governmental levels, including the establishment of a governance framework and training provision. (shrink)

The popularisation of Artificial Intelligence (AI) technologies has sparked discussion about their ethical implications. This development has forced governmental organisations, NGOs, and private companies to react and draft ethics guidelines for future development of ethical AI systems. Whereas many ethics guidelines address values familiar to ethicists, they seem to lack in ethical justifications. Furthermore, most tend to neglect the impact of AI on democracy, governance, and public deliberation. Existing research suggest, however, that AI can threaten key elements of (...) western democracies that are ethically relevant. In this paper, Rawls’s theory of justice is applied to draft a set of guidelines for organisations and policy-makers to guide AI development towards a more ethical direction. The goal is to contribute to the broadening of the discussion on AI ethics by exploring the possibility of constructing AI ethics guidelines that are philosophically justified and take a broader perspective of societal justice. The paper discusses how Rawls’s theory of justice as fairness and its key concepts relate to the ongoing developments in AI ethics and gives a proposition of how principles that offer a foundation for operationalising AI ethics in practice could look like if aligned with Rawls’s theory of justice as fairness. (shrink)

We present a training module in AI ethics designed to prepare a broad group of professionals to recognize and address potential ethical challenges of AI applications in healthcare. Training materials include a two-page checklist, a brief glossary, and three practical case studies. While we have developed and applied this framework for training Research Ethics Committee members in France and South Africa, it can also be helpful in university courses ranging from public health and healthcare law to biomedical engineering (...) and applied ethics. (shrink)

Current advances in research, development and application of artificial intelligence systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI (...) ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved. (shrink)

We paraphrase Descartes’ famous dictum in the area of AI ethics where the “I doubt and therefore I am” is suggested as a necessary aspect of morality. Therefore AI, which cannot doubt itself, cannot possess moral agency. Of course, this is not the end of the story. We explore various aspects of the human mind that substantially differ from AI, which includes the sensory grounding of our knowing, the act of understanding, and the significance of being able to doubt (...) ourselves. The foundation of our argument is the discipline of ethics, one of the oldest and largest knowledge projects of human history, yet, we seem only to be beginning to get a grasp of it. After a couple of thousand years of studying the ethics of humans, we (humans) arrived at a point where moral psychology suggests that our moral decisions are intuitive, and all the models from ethics become relevant only when we explain ourselves. This recognition has a major impact on what and how we can do regarding AI ethics. We do not offer a solution, we explore some ideas and leave the problem open, but we hope somewhat better understood than before our study. (shrink)

Technological outsourcing is increasingly prevalent, with AI systems taking over many tasks once performed by humans. This shift has led to various discussions within AI ethics. A question that was largely ignored until recently, but is now increasingly being discussed, concerns the meaningfulness of such a lifestyle. The literature largely features skeptical views, raising several challenges. Many of these challenges can be grouped under what I identify as the “meaningfulness gap”. Although this gap is widely acknowledged, there is a (...) notable absence of systematic exploration in the literature. This paper aims to fill this void by offering a detailed, step-by-step guide for systematically exploring the different instances of the meaningfulness gap and aids in navigating their complexities. More specifically, it proposes differentiating the gaps according to their realms and objects, normative nature, scope, and severity. To make these areas manageable, the paper takes several taxonomies and distinctions on board. Finally, the guide is summarized, and some skeptical replies are anticipated and countered by clarificatory remarks. (shrink)

If computer ethics is to constitute a real engagement with industry and society that cultivates a genuine sensitivity to ethical concerns in the creation, development, and implementation of technologies, a genuine sensitivity that stands in marked contrast to ethics as “mere compliance,” then computer ethics will have to consist in issuing an open invitation to inquiry, since going beyond mere compliance requires a sensitivity to the importance of what we care about, and inquiry has the (...) potential to leverage what our audiences already care about to bring them to care about computer ethics. This commitment to open inquiry is apparent in the practice of computer ethics at Ethicomp and in our interfaces with various audiences beyond Ethicomp. We mentor professionals, aspiring professionals, managers, end-users, policy makers, and each other by offering an open invitation to inquiry about the broad ethical implications of the practices, professions, and institutions of computing and the design, development, and implementation of computer artifacts. (shrink)

Several competing narratives drive the contemporary AI ethics discourse. At the two extremes are sociotechnical dogmatism, which holds that society is full of inefficiencies and imperfections that can only be solved by better technology; and sociotechnical skepticism, which highlights the unacceptable risks AI systems pose. While both narratives have their merits, they are ultimately reductive and limiting. As a constructive synthesis, we introduce and defend sociotechnical pragmatism—a narrative that emphasizes the central role of context and human agency in designing (...) and evaluating emerging technologies. In doing so, we offer two novel contributions. First, we demonstrate how ethical and epistemological considerations are intertwined in the AI ethics discourse by tracing the dialectical interplay between dogmatic and skeptical narratives across disciplines. Second, we show through examples how sociotechnical pragmatism does more to promote fair and transparent AI than dogmatic or skeptical alternatives. By spelling out the assumptions that underpin sociotechnical pragmatism, we articulate a robust stance for policymakers and scholars who seek to enable societies to reap the benefits of AI while managing the associated risks through feasible, effective, and proportionate governance. (shrink)

Ethics regulation for human-subject research (HSR) has been established for about 20 years in Brazil. However, compliance with this regulation is controversial for non-biomedical sciences, particularly for human and social sciences (HSS), the source of a recent debate at the National Commission for Research Ethics. We hypothesized that for these fields, formal requirements for compliance with HSR regulation in graduate programs, responsible for the greatest share of Brazilian science, would be small in number. We analyzed institutional (...) documents (collected from June 2014 to May 2015) from 171 graduate programs at six prestigious Brazilian universities in São Paulo and Rio de Janeiro, the states that fund most of the science conducted in Brazil. Among these programs, 149 were in HSS. The results suggest that non-compliance with standard regulation seems to be the rule in most of these programs. The data may reflect not only a resistance from scientists in these fields to comply with standard regulations for ethics in HSR but also a disciplinary tradition that seems prevalent when it comes to research ethics in HSR. However, recent encounters between Brazilian biomedical and non-biomedical scientists for debates over ethics in HSR point to a changing culture in the approach to research ethics in the country. (shrink)

This paper defends two main claims. First, that political philosophy deserves a central place in AI Ethics’ curricula. This is a claim about the content of the AI Ethics class. The second claim is about the form of the AI Ethics class: namely, that considerations originating in political philosophy must inform the way in which AI Ethics is taught. The basic idea animating both claims, is that AI has powerful political implications and that preparing students to (...) navigate these implications requires paying close attention to both the cognitive and practical learning goals of the AI Ethics course. (shrink)