Luciano Floridi

Artificial intelligence’s impact on healthcare is undeniable. What is less clear is whether it will be ethically justifiable. Just as we know that AI can be used to diagnose disease, predict risk, develop personalized treatment plans, monitor patients remotely, or automate triage, we also know that it can pose significant threats to patient safety and the reliability (or trustworthiness) of the healthcare sector as a whole. These ethical risks arise from (a) flaws in the evidence base of healthcare AI (epistemic concerns); (b) the potential of AI to transform fundamentally the meaning of health, the nature of healthcare, and the practice of medicine (normative concerns); and (c) the “black box” nature of the AI development pipeline, which undermines the effectiveness of existing accountability mechanisms (traceability concerns). In this chapter, we systematically map (a)–(c) to six different levels of abstraction: individual, interpersonal, group, institutional, sectoral, and societal. The aim is to help policymakers, regulators, and other high-level stakeholders delineate the scope of regulation and other “softer” governing measures for AI in healthcare. We hope that by doing so, we may enable global healthcare systems to capitalize safely and reliably on the many benefits of healthcare AI.

_Presenting an array of innovative concepts to revolutionize politics in our digital world, this book is written by one of the most authoritative voices of contemporary philosophy_ _The Green and the Blue_ explores the opportunities presented by the digital age for combining _green_ environmental policies with _blue_ digital solutions to strengthen democracy, reform capitalism, and work toward a sustainable and equitable future. With an engaging and readable style, world-renowned philosopher Luciano Floridi lays out a timely and convincing case for embracing responsible practices to ensure a sustainable environment, a better democracy, and an equitable information society. Drawing from his expertise in digital ethics and the philosophy of technology, Floridi offers fresh perspectives, innovative ideas, and bold strategies for improving political systems that emphasize the importance of cooperation, ethics, and long-term planning. Throughout the text, the author advocates for a political and economic framework of care that focuses on the quality of relations and processes rather than consumption and things. A groundbreaking call to action for individuals, policymakers, and technologists, _The Green and the Blue: Naive Ideas to Improve Politics in the Digital Age:_ Provides a comprehensive, interdisciplinary, and well-rounded exploration of how to improve the present by designing good policies for the future Features insightful analysis by the founder of the philosophy of information and one of the key interpreters of the digital revolution Explains complex concepts to allow easy understanding and application of the ideas presented Includes compelling thought experiments that challenge traditional notions of capitalism and politics Encourages readers to think critically about the present and future of politics Offering a new way of thinking about democracy, the environment, and technology, _The Green and the Blue: Naive Ideas to Improve Politics in the Digital Age_ is a must-read for undergraduate and graduate students, practitioners, and general readers who are seeking positive and sustainable change in the world of politics.

_This _Guide_ provides an ambitious state-of-the-art survey of the fundamental themes, problems, arguments and theories constituting the philosophy of computing._ A complete guide to the philosophy of computing and information. Comprises 26 newly-written chapters by leading international experts. Provides a complete, critical introduction to the field. Each chapter combines careful scholarship with an engaging writing style. Includes an exhaustive glossary of technical terms. Ideal as a course text, but also of interest to researchers and general readers.

A compilation of cutting-edge, comprehensive insights into digital ethics from leading scholars As digital technologies shape every aspect of today's society, ethical considerations have never been more pressing. In A Companion to Digital Ethics, editors Luciano Floridi and Mariarosaria Taddeo bring together leading experts to analyse key ethical challenges posed by artificial intelligence, privacy, cybersecurity, cyberwarfare, sustainability, digital consent, and many other topics. With a multidisciplinary approach, this authoritative volume introduces all the relevant topics in digital ethics clearly and accessibly, integrating insights from philosophy, law, computer science, and the social sciences. Unlike other collections that focus on specialised research or introductory overviews, this Companion strikes a critical balance—offering a systematic, comprehensive, simple yet rigorous examination of digital ethics that is both conceptually rich and practically relevant. Each chapter can be read as part of the whole or independently. Throughout the book, the contributing authors equip the reader with diverse perspectives for navigating ethical dilemmas in the evolving digital landscape, with an essential list of references and further readings. Integrating real-world examples and case studies throughout, A Companion to Digital Ethics: Explores the ethical value and implications of digital technologies and their applications Offers clear and precise analysis of both current and potential ethical challenges Includes discussions of ethical issues in contexts of technology governance and digital law Helps professionals and policymakers implement ethical principles in AI and digital technologies Contains authoritative and cutting-edge chapters, edited by two leading experts in digital ethics Whether used in academic courses or professional discussions on responsible digital innovation, A Companion to Digital Ethics is an essential guide to understanding and addressing the ethical questions of the digital age. It is a must-have resource for undergraduate and postgraduate students in any academic discipline, as well as policymakers and practitioners seeking to implement ethical frameworks for emerging technologies.

This is the first of two volumes of the proceedings from the 30th International Wittgenstein Symposium in Kirchberg, August 2007. In addition to several new contributions to Wittgenstein research, this volume contains articles with a special focus on digital Wittgenstein research and Wittgenstein's role for the understanding of the digital turn, as well as discussions - not necessarily from a Wittgensteinian perspective - about issues in the philosophy of information, including computational ontologies.

Brain-computer interfaces (BCIs) offer significant therapeutic opportunities for a variety of neurophysiological and neuropsychiatric disorders and may perhaps one day lead to augmenting the cognition and decision-making of the healthy brain. However, existing regulatory frameworks designed for implantable medical devices (IMDs) are inadequate to address the unique ethical, legal, and social risks associated with next-generation networked brain-computer interfaces (BCIs). In this article, we make nine recommendations to support developers in the design of BCIs and nine recommendations to support policymakers in the application of BCIs, drawing insights from the regulatory history of IMDs and principles from AI ethics. We begin by outlining the historical development of IMDs and the regulatory milestones that have shaped their oversight. Next, we summarize similarities between IMDs and emerging implantable BCIs, identifying existing provisions for their regulation. We then use two case studies of emerging cutting-edge BCIs-the HALO (Hardware Architecture for LOw-power BCIs) and SCALO (SCalable Architecture for LOw-power BCIs) computer systems-to highlight distinctive features in the design and application of nextgeneration BCIs arising from contemporary chip architectures, which necessitate reevaluating regulatory approaches. We identify critical ethical considerations for these BCIs, including unique conceptions of autonomy, identity, and mental privacy. Based on these insights, we suggest potential avenues for the ethical regulation of BCIs, emphasizing the importance of interdisciplinary collaboration and proactive mitigation of potential harms. The goal is to support the responsible design and application of new BCIs, ensuring their safe and ethical integration into medical practice.

The article introduces the concept of “semantic pareidolia” - our tendency to attribute consciousness, intelligence, and emotions to AI systems that lack these qualities. It examines how this psychological phenomenon leads us to perceive meaning and intentionality in statistical pattern-matching systems, similar to seeing faces in clouds. It analyses the converging forces intensifying this tendency: increasing digital immersion, profit-driven corporate interests, social isolation, and AI advancement. The article warns of progression from harmless anthropomorphism to problematic AI idolatry, and calls for responsible design practices that help users maintain critical distinctions between simulation and genuine consciousness. It is the English translation and adaptation of an article originally published in Italian in Harvard Business Review Italia, June 2025.

This article introduces a systematic methodology, called red reading, for critical textual analysis that adapts red teaming techniques from computer science and artificial intelligence (AI) into a comprehensive diagnostic protocol, supported by Large Language Models (LLMs), to analyse written documents. As the methodological counterpart of distant writing, which transforms text production through AI-assisted design, red reading expands human analytical capabilities and critical reading by embedding adversarial analysis in a constructive framework powered by LLMs. The approach employs a rigorously tested, diagnostic protocol that stresstests texts across structural, rhetorical, logical, and stylistic dimensions, while strengthening interpretive understanding. The methodology shows how LLM-supported, textual red teaming can provide a rigorous, critical and constructive approach to textual analysis with broad applications across disciplines. We exemplify the method by developing a fifteen-phase diagnostic prompt for red reading. In the Appendix, we apply this protocol to a case study analysing Turing's seminal article, Computing Machinery and Intelligence. We finally discuss the challenges of using red reading and provide some suggestions for future research. In particular, we emphasise the importance of calibration methods for LLMs' critical analysis, along with evidence-based strategies for seamlessly integrating red reading into real-world workflows.

The proliferation of generative AI systems has created new challenges for the Free and Open Source Software (FOSS) community, particularly regarding how traditional copyleft principles should apply when open source code is used to train AI models. This article introduces the Contextual Copyleft AI (CCAI) license, a novel licensing mechanism that extends copyleft requirements from training data to the resulting generative AI models. The CCAI license offers significant advantages, including enhanced developer control, incentivization of open source AI development, and mitigation of openwashing practices. This is demonstrated through a structured three-part evaluation framework that examines (1) legal feasibility under current copyright law, (2) policy justification comparing traditional software and AI contexts, and (3) synthesis of cross-contextual benefits and risks. However, the increased risk profile of open source AI, particularly the potential for direct misuse, necessitates complementary regulatory approaches to achieve an appropriate risk-benefit balance. The paper concludes that when implemented within a robust regulatory environment focused on responsible AI usage, the CCAI license provides a viable mechanism for preserving and adapting core FOSS principles to the evolving landscape of generative AI development.

Artificial Intelligence (AI) presents unprecedented opportunities to transform healthcare worldwide, from improving diagnostic accuracy to expanding access in underserved regions. Despite this potential and growing investment, a significant gap persists between AI’s theoretical promise and its realised benefits in healthcare settings. This article examines the complex barriers impeding AI benefits realization in global health contexts, including ethical uncertainties, data infrastructure limitations, evidence quality concerns, and regulatory ambiguities. We analyze current initiatives addressing these challenges and highlight how technological solutions alone cannot resolve fundamental healthcare inequities. Drawing on the interdisciplinary perspectives and insights presented at the Global Health in the Age of AI Symposium hosted by the Cini Foundation and Yale Digital Ethics Center, we propose five core infrastructure requirements necessary for ethical AI implementation: robust data exchange; epistemic certainty with staff autonomy; actively protected healthcare values; validated outcomes with meaningful accountability; and environmental sustainability. These requirements form the foundation for a systems approach that balances technological advancement with ethical imperatives, contextual adaptability, and global equity considerations. We conclude that the successful integration of AI into healthcare demands coordinated action across sectors and borders, with careful attention to avoiding technological colonialism and ensuring AI serves as a force for health equity rather than widening existing disparities.

Brain-computer interfaces (BCIs) show enormous potential for advancing personalized medicine. However, BCIs also introduce new avenues for cyber-attacks or security compromises. In this article, we analyze the problem and make recommendations for device manufacturers to better secure devices and to help regulators understand where more guidance is needed to protect patient safety and data confidentiality. Device manufacturers should implement the prior suggestions in their BCI products. These recommendations help protect BCI users from undue risks, including compromised personal health and genetic information, unintended BCI-mediated movement, and many other cybersecurity breaches. Regulators should mandate non-surgical device update methods, strong authentication and authorization schemes for BCI software modifications, encryption of data moving to and from the brain, and minimize network connectivity where possible. We also design a hypothetical, average-case threat model that identifies possible cybersecurity threats to BCI patients and predicts the likeliness of risk for each category of threat. BCIs are at less risk of physical compromise or attack, but are vulnerable to remote attack; we focus on possible threats via network paths to BCIs and suggest technical controls to limit network connections.

Regulating emerging technologies involves balancing the mitigation of risks with the promotion of innovation; a balance frequently seen as a zero-sum "dilemma of control". Regulatory sandboxes offer a practical way to address this dilemma by enabling controlled, evidence-based testing of new technologies. In this article, we examine the regulatory sandbox framework introduced by the EU Artificial Intelligence Act (AIA). We argue that the AIA's multi-level governance structure represents a shift from traditional sandbox models by prioritizing regulatory learning over technological disruption and expanding public interest considerations to include strategically aligned commercial innovations. Afterwards, we identify governance challenges across three sandbox phases-pre-testing, testing, and post-testing-and propose structured solutions. Our analysis suggests that effective sandbox governance requires specific mechanisms: tailored entry criteria, precise pipeline placement guidance, and multi-agency coordination in pre-testing; experimental realism and continuous risk classification updates during testing; and clear graduation criteria with robust transition support in post-testing.

This article introduces the concept of “distant writing”, a novel literary practice in which authors act as designers, employing Large Language Models (LLMs) to generate narratives, while retaining creative control through precise prompting and iterative refinement. Unlike Moretti’s _distant reading_, which uses computational analysis to interpret large corpora of existing texts, distant writing harnesses computational tools (LLMs) to author new narratives, reshaping the literary production process. By examining theoretical frameworks and practical consequences, and running an experiment in distant writing called _Encounters_, this article argues that distant writing represents a significant evolution in authorship, not replacing but expanding human creativity within a design paradigm. The distinction between writing (close) and “wrAIting” (distant) reveals how LLM-assisted creativity can generate narrative possibilities previously inaccessible, transforming literature’s modal space while challenging traditional notions of authorship, creativity, and literary production. This emerging practice merits critical attention as it shapes future literary landscapes and reconfigures relationships between human creativity and artificial intelligence.

The article has the goal of indicating how to harness the potential for good of artificial intelligence (AI) – defined as a distinct form of autonomous and self- learning agency and thus raises unique ethical challenges – while mitigating its ethical challenges. The analyses focuses first on uses of AI that may lead to undue discrimination, lack of explainability, the responsibility gap, and the nudging potential of AI and its negative impact on human self-determination. It then turns on the role that ethical analyses in harnessing the potential for good of AI and argues that existing guidelines for the ethics design, development and use of AI will be effective insofar as they are translated into viable guidelines to shape AI-based innovation and that this is the task of digital ethics as a translational ethics.

The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Samuel in Science, 132(3429):741–742, 1960. Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles—the ‘what’ of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)—rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

In this chapter, I introduce the concept of “digital ectype”. An ectype is a copy that has a special relation with its source (the origin of its creation), the archetype, like the impression left by a seal. It is not the real thing, but it is clearly linked in a significant, authentic way with the real thing itself. I argue that digital technologies are able to separate the archetypal source – what was in the mind of the artist, for example – from the process (style, method, procedure) that leads from the source to the artefact. Once this link is severed, one can have digital ectypes that are “authentic” in style and content, but not “original”, in terms of archetypal source, and digital ectypes that are “original” in terms of archetypal source (they do come from where they purport to come) yet not “authentic” in terms of production, performance, or method (they are not the ones used by the source to deliver the artefact). In other words, digital ectypes can be authentic but unoriginal artefacts, or inauthentic but original artefacts.

This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Five literature databases were searched (Scopus, Google Scholar, Philpapers, Web of Science, Pub Med), in April 2019, to support the following research question: “how can the primary ethical risks presented by AI-health be categorised, and what issues must policymakers, regulators and developers consider in order to be ‘ethically mindful?’”. A series of screening stages were carried out—for example, removing articles that focused on digital health in general (e.g. data sharing, data access, data privacy, surveillance/nudging, consent, ownership of health data, evidence of efficacy)—yielding a total of 156 papers that were included in the review.We find that ethical issues can be (a) epistemic, related to misguided, inconclusive or inscrutable evidence; (b) normative, related to unfair outcomes and transformative effectives; or (c) related to traceability. We further find that these ethical issues arise at six levels of abstraction: individual, interpersonal, group, institutional, and societal or sectoral. Finally, we outline a number of considerations for policymakers and regulators, mapping these to existing literature, and categorising each as epistemic, normative or traceability-related and at the relevant level of abstraction. This article contributes to the debate on AI in health care by offering a comprehensive analysis of the relevant literature, focusing on the ethical implications for individuals, interpersonal relationships, groups, institutions, societies and the health sector as a whole. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms. We argue that if action is not swiftly taken in this regard, a new ‘AI winter’ could occur due to chilling effects related to a loss of public trust in the benefits of AI for health care.