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Regulation theory offers a unique perspective on the institutional aspects of digital capitalism’s accumulation regime. However, a gap exists in examining the associated mode of regulation. Based on the analysis of AI ethics washing phenomenon, we suggest the state is delicately balancing between fueling innovation and reducing uncertainty in emerging technologies. This balance leads to a unique mode of regulation, "Fugazi regulation," characterized by vaguely defined, non-enforceable moral principles with no specific implementation mechanisms. We propose a microeconomic model that rationalizes (...) |
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The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks. These risks often differ from those associated with traditional discriminative machine learning. To synthesize the recent discourse and map its normative concepts, we conducted a scoping review on the ethics of generative artificial intelligence, including especially large language models and text-to-image models. Our analysis provides a taxonomy of 378 normative issues in 19 topic areas and ranks them (...) |
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In this article, we use the account of the “inhuman” that is developed in the work of the French philosopher Jean-François Lyotard to develop a critique of human-centred AI. We argue that Lyotard’s philosophy not only provides resources for a negative critique of human-centred AI discourse, but also contains inspiration for a more constructive account of how the discourse around human-centred AI can take a broader view of the human that includes key dimensions of Lyotard’s inhuman, namely performativity, vulnerability, and (...) |
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With the growing application of machine learning models in medicine, principlist bioethics has been put forward as needing revision. This paper reflects on the dominant trope in AI ethics to include a new ‘principle of explicability’ alongside the traditional four principles of bioethics that make up the theory of principlism. It specifically suggests that these four principles are sufficient and challenges the relevance of explicability as a separate ethical principle by emphasizing the coherentist affinity of principlism. We argue that, through (...) |
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Health Recommender Systems are promising Articial-Intelligence-based tools endowing healthy lifestyles and therapy adherence in healthcare and medicine. Among the most supported areas, it is worth mentioning active aging. However, current HRS supporting AA raise ethical challenges that still need to be properly formalized and explored. This study proposes to rethink HRS for AA through an autonomy-based ethical analysis. In particular, a brief overview of the HRS’ technical aspects allows us to shed light on the ethical risks and challenges they might (...) |
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As artificial intelligence (AI) integrates across social domains, prevailing technical paradigms often overlook human relational needs vital for cooperative resilience. Alternative pathways consciously supporting dignity and wisdom warrant consideration. Integrating seminal insights from virtue and care ethics, this article delineates the following four cardinal design principles prioritizing communal health: (1) affirming the sanctity of life; (2) nurturing healthy attachment; (3) facilitating communal wholeness; and (4) safeguarding societal resilience. Grounding my analysis in the rich traditions of moral philosophy, I argue that (...) |
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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 (...) |
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Critics currently argue that applied ethics approaches to artificial intelligence (AI) are too principles-oriented and entail a theory–practice gap. Several applied ethical approaches try to prevent such a gap by conceptually translating ethical theory into practice. In this article, we explore how the currently most prominent approaches of AI ethics translate ethics into practice. Therefore, we examine three approaches to applied AI ethics: the embedded ethics approach, the ethically aligned approach, and the Value Sensitive Design (VSD) approach. We analyze each (...) |
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The rapid diffusion of artificial intelligence (AI) technologies in the defence domain raises challenges for the ethical governance of these systems. A recent shift from the what to the how of AI ethics sees a nascent body of literature published by defence organisations focussed on guidance to implement AI ethics principles. These efforts have neglected a crucial intermediate step between principles and guidance concerning the elicitation of ethical requirements for specifying the guidance. In this article, we outline the key normative (...) |
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Responsible AI (RAI) guidelines aim to ensure that AI systems respect democratic values. While a step in the right direction, they currently fail to impact practice. Our work discusses reasons for this lack of impact and clusters them into five areas: (1) the abstract nature of RAI guidelines, (2) the problem of selecting and reconciling values, (3) the difficulty of operationalising RAI success metrics, (4) the fragmentation of the AI pipeline, and (5) the lack of internal advocacy and accountability. Afterwards, (...) |
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Background While the theoretical benefits and harms of Artificial Intelligence (AI) have been widely discussed in academic literature, empirical evidence remains elusive regarding the practical ethical challenges of developing AI for healthcare. Bridging the gap between theory and practice is an essential step in understanding how to ethically align AI for healthcare. Therefore, this research examines the concerns and challenges perceived by experts in developing ethical AI that addresses the healthcare context and needs. Methods We conducted semi-structured interviews with 41 (...) |
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The effects that artificial intelligence (AI) technologies will have on society in the short- and long-term are inherently uncertain. For this reason, many governments are avoiding strict command and control regulations for this technology and instead rely on softer ethics-based approaches. The Russian approach to regulating AI is characterized by the prevalence of unenforceable ethical principles implemented via industry self-regulation. We analyze the emergence of the regulatory regime for AI in Russia to illustrate the limitations of this approach. The article (...) |
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As the artificial intelligence (AI) ethics field is currently working towards its operationalisation, ethics review as carried out by research ethics committees (RECs) constitutes a powerful, but so far underdeveloped, framework to make AI ethics effective in practice at the research level. This article contributes to the elaboration of research ethics frameworks for research projects developing and/or using AI. It highlights that these frameworks are still in their infancy and in need of a structure and criteria to ensure AI research (...) |
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The present article aims to discuss the possibility of including the sphere of artificial intelligence production within the domain of artificial intelligence ethics and investigate its moral implications. In the first section, the role of human labour in the artificial intelligence production processes is considered, with particular reference to the distinction between high-skilled and low-skilled jobs, their differential distribution in the production process itself, and the labour conditions of ghost workers, in order to analyse the main ethical issues emerging within (...) |
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As AI technologies are increasingly deployed in work, welfare, healthcare, and other domains, there is a growing realization not only of their power but of their problems. AI has the capacity to reinforce historical injustice, to amplify labor precarity, and to cement forms of racial and gendered inequality. An alternate set of values, paradigms, and priorities are urgently needed. How might we design and evaluate AI from an indigenous perspective? This article draws upon the five Tests developed by Māori scholar (...) |
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What happens when there is not enough data to train machine learning algorithms? In recent years, so-called ‘synthetic data’ have been increasingly used to add to or supplement the training regimes of various machine learning algorithms. Seeking to read the notion of supplementarity differently through an engagement with the work of Jacques Derrida, I propose that the nascent emergence of synthetic data embodies what I call the logic of the synthetic supplement in algorithmic societies. I argue, on the one hand, (...) |
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Our research is a value sensitive based approach to designing autonomous systems in a military context. Value sensitive design is an iterative process of conceptual, empirical and technical considerations. We enhance value sensitive design with Participatory Value Evaluation. This allows us to mine values of a large unorganized stakeholder group relevant to our context of research, namely Australian citizens. We found that value prioritizations differ depending on the context of use and that no one value fits all autonomous systems. General (...) |
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While the technologies that enable Artificial Intelligence (AI) continue to advance rapidly, there are increasing promises regarding AI’s beneficial outputs and concerns about the challenges of human–computer interaction in healthcare. To address these concerns, institutions have increasingly resorted to publishing AI guidelines for healthcare, aiming to align AI with ethical practices. However, guidelines as a form of written language can be analyzed to recognize the reciprocal links between its textual communication and underlying societal ideas. From this perspective, we conducted a (...) |
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