Luciano Floridi

This article examines the feasibility and strategic advantages of developing open-source (OS) foundation models within the European Union (EU). Drawing upon recent developments in AI strategies globally, particularly in China, we argue that the EU’s robust regulatory framework and commitment to ethical principles uniquely position it to produce trustworthy OS foundation models. Due to stringent regulatory compliance, EU-developed OS foundation models would inherently ensure greater trustworthiness, offering a competitive and ethically aligned alternative to proprietary and foreign models. We conclude by offering concrete recommendations to realise this strategic opportunity through EU initiatives such as the AI Factories and EuroHPC infrastructure, emphasising the EU’s potential role in shaping global AI standards.

Artificial intelligence (AI) assurance is an umbrella term describing many approaches—such as impact assessment, audit, and certification procedures—used to provide evidence that an AI system is legal, ethical, and technically robust. AI assurance approaches largely focus on two overlapping categories of harms: deployment harms that emerge at, or after, the point of use, and individual harms that directly impact a person as an individual. Current approaches generally overlook upstream collective and societal harms associated with the development of systems, such as resource extraction and processing, exploitative labour practices and energy intensive model training. Thus, the scope of current AI assurance practice is insufficient for ensuring that AI is ethical in a holistic sense, i.e. in ways that are legally permissible, socially acceptable, economically viable and environmentally sustainable. This article addresses this shortcoming by arguing for a broader approach to AI assurance that is sensitive to the full scope of AI development and deployment harms. To do so, the article maps harms related to AI and highlights three examples of harmful practices that occur upstream in the AI supply chain and impact the environment, labour, and data exploitation. It then reviews assurance mechanisms used in adjacent industries to mitigate similar harms, evaluating their strengths, weaknesses, and how effectively they are being applied to AI. Finally, it provides recommendations as to how a broader approach to AI assurance can be implemented to mitigate harms more effectively across the whole AI supply chain.

This article examines potential regulatory pathways for AI in the United States following the Trump administration's 2025 revocation of the Biden-era AI Executive Order. We outline two competing governance scenarios: decentralized state-level regulation (with minimal federal oversight) and centralized federal dominance (through legislative pre-emption). We critically evaluate each model's policy implications, constitutional challenges, and practical trade-offs, particularly regarding innovation and state autonomy. We argue that AI's technological characteristics and context-dependent nature complicate achieving regulatory coherence amid competing federal and state interests. As a result, even under the Trump administration's broader deregulatory agenda, targeted federal intervention may remain necessary.

Deliberative democracy relies on well-designed institutional frameworks-like participant selection, facilitation, and decision-making. Yet identifying the best design for a given context is challenging, as real-world and lab-based studies are often costly, time-consuming, and difficult to replicate. This commentary proposes Digital Twin (DT) technology as a regulatory sandbox for deliberative democracy. By simulating dynamic, data-driven models of real or synthetic communities, DTs allow researchers and policymakers to test alternative designs through controlled "what-if" scenarios, free from real-world constraints. The commentary also examines limitations and key directions for future research.

Deliberative democracy depends on carefully designed institutional frameworks — such as participant selection, facilitation methods, and decision-making mechanisms — that shape how deliberation performs. However, identifying optimal institutional designs for specific contexts remains challenging when relying solely on real-world observations or laboratory experiments: they can be expensive, ethically and methodologically tricky, or too limited in scale to give us clear answers. Computational experiments offer a complementary approach, enabling researchers to conduct large-scale investigations while systematically analyzing complex dynamics, emergent and unexpected collective behavior, and risks or opportunities associated with novel democratic designs. Therefore, this paper explores Digital Twin (DT) technology as a computational testing ground for deliberative systems (with potential applicability to broader institutional analysis). By constructing dynamic models that simulate real-world deliberation, DTs allow researchers and policymakers to rigorously test "what-if" scenarios across diverse institutional configurations in a controlled virtual environment. This approach facilitates evidence-based assessment of novel designs using synthetically generated data, bypassing the constraints of real-world or lab-based experimentation, and without societal disruption. The paper also discusses the limitations of this new methodological approach and suggests where future research should focus.

There are more than 350,000 health apps available in public app stores. The extolled benefits of health apps are numerous and well documented. However, there are also concerns that poor-quality apps, marketed directly to consumers, threaten the tenets of evidence-based medicine and expose individuals to the risk of harm. This study addresses this issue by assessing the overall quality of evidence publicly available to support the effectiveness claims of health apps marketed directly to consumers. To assess the quality of evidence available to the public to support the effectiveness claims of health apps marketed directly to consumers, an audit was conducted of a purposive sample of apps available on the Apple App Store. We find the quality of evidence available to support the effectiveness claims of health apps marketed directly to consumers to be poor. Less than half of the 220 apps (44%) we audited state that they have evidence to support their claims of effectiveness and, of these allegedly evidence-based apps, more than 70% rely publicly on either very low or low-quality evidence. For the minority of app developers that do publish studies, significant methodological limitations are commonplace. Finally, there is a pronounced tendency for apps—particularly mental health and diagnostic apps—to either borrow evidence generated in other (typically offline) contexts or to rely exclusively on unsubstantiated, unpublished user metrics as evidence to support their effectiveness claims. Health apps represent a significant opportunity for individual consumers and healthcare systems. Nevertheless, this opportunity will be missed if the health apps market continues to be flooded by poor quality, poorly evidenced, and potentially unsafe apps. It must be accepted that a continuing lag in generating high-quality publicly available evidence of app effectiveness and safety is not inevitable: it is a choice. Just because it will be challenging to raise the quality of the evidence base publicly available to support the claims of health apps, this does not mean that the bar for evidence quality should be lowered. Innovation for innovation’s sake must not be prioritized over public health and safety.

Recommender Systems (RS) on digital platforms increasingly influence user behavior, raising ethical concerns, privacy risks, harmful content promotion, and diminished user autonomy. This article examines RS within the framework of regulations and lawsuits in the United States and advocates for legislation that can withstand constitutional scrutiny under First Amendment protections. We propose (re)framing RS-curated content as commercial speech, which is subject to lessened free speech protections. This approach provides a practical path for future legislation that would allow for effective oversight of RS, particularly in areas of substantial public interest like child safety, national security, and misinformation.

This article presents the first thematic review of the literature on the ethical issues concerning digital well-being. The term ‘digital well-being’ is used to refer to the impact of digital technologies on what it means to live a life that is good for a human being. The review explores the existing literature on the ethics of digital well-being, with the goal of mapping the current debate and identifying open questions for future research. The review identifies major issues related to several key social domains: healthcare, education, governance and social development, and media and entertainment. It also highlights three broader themes: positive computing, personalised human–computer interaction, and autonomy and self-determination. The review argues that three themes will be central to ongoing discussions and research by showing how they can be used to identify open questions related to the ethics of digital well-being.

In July 2017, China’s State Council released the country’s strategy for developing artificial intelligence, entitled ‘New Generation Artificial Intelligence Development Plan’. This strategy outlined China’s aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China’s AI policies or have assessed the country’s technical capabilities. Instead, in this article, we focus on the socio-political background and policy debates that are shaping China’s AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. By focusing on the policy backdrop, we seek to provide a more comprehensive and critical understanding of China’s AI policy by bringing together debates and analyses of a wide array of policy documents.

To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decisionmaking processes. Using US mortgage lending as an example use case, we discuss the ethical foundations of each definition of fairness and demonstrate that our proposed methodology more closely captures the ethical trade-offs of the decision-maker, as well as forcing a more explicit representation of which values and objectives are prioritised.

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.

This article advances an explanatory model of the academic and policy debate on AI as legal persons. It argues that the scientific and regulatory debate on AI as legal persons undergoes periods of relative stability interrupted by rapid paradigm shifts. Three interrelated factors primarily influence these oscillations: (1) competing theories of legal personhood (clustered versus singularist), (2) capability, embodiment, and commercial reach of AI technology, and (3) AI's integration within socio-digital institutions. Two additional forces modulate the depth and durability of any shift: (4) the overlap and internal consistency of cross‑domain legal regimes (data protection, agency, liability, cybersecurity); and (5) the body of judicial precedents (or their scarcity) on extending personhood. Specific convergences of these factors create pressure for status reconsideration; absent them, equilibrium returns. Current EU law and case practice are explained by partial convergence—capability/diffusion surges and some cluster‑theory uptake without sufficiently embedding AI in rights‑bearing institutional roles—and by the damping effect of dense, interlocking legal regimes, plus scarce and negative judicial precedents. Case law suggests that, without explicit legislation, courts are unlikely to grant AI legal personhood on their own. Prospective movement would require demonstrable de facto social personhood: sustained, role‑based participation within socio-digital institutions that creates governance gaps that existing doctrines cannot internalize. The article concludes by assessing near- and long‑term prospects — from generative AI, autonomous agents, social robots, and digital twins to brain–machine interface — highlighting conditional pathways rather than deterministic trajectories.

Artificial intelligence (AI) is rapidly reshaping our world. As AI systems become increasingly autonomous and integrated into various sectors, fundamental ethical issues such as accountability, transparency, bias, and privacy are exacerbated or morph into new forms. This introduction provides an overview of the current ethical landscape of AI. It explores the pressing need to address biases in AI systems, protect individual privacy, ensure transparency and accountability, and manage the broader societal impacts of AI on labor markets, education, and social interactions. It also highlights the global nature of AI's challenges, such as its environmental impact and security risks, stressing the importance of international collaboration and culturally sensitive ethical guidelines. It then outlines three unprecedented challenges AI poses to copyright and intellectual property rights; individual autonomy through AI's “hypersuasion”; and our understanding of authenticity, originality, and creativity through the transformative impact of AI-generated content. The conclusion emphasizes the importance of ongoing critical vigilance, imaginative conceptual design, and collaborative efforts between diverse stakeholders to deal with the ethical complexities of AI and shape a sustainable and socially preferable future. It underscores the crucial role of philosophy in identifying and analyzing the most significant problems and designing convincing and feasible solutions, calling for a new, engaged, and constructive approach to philosophical inquiry in the digital age.

Machine unlearning (MU) is often analyzed in terms of how it can facilitate the “right to be forgotten.” In this commentary, we show that MU can support the OECD’s five principles for trustworthy AI, which are influencing AI development and regulation worldwide. This makes it a promising tool to translate AI principles into practice. We also argue that the implementation of MU is not without ethical risks. To address these concerns and amplify the positive impact of MU, we offer policy recommendations across six categories to encourage the research and uptake of this potentially highly influential new technology.

Healthcare systems are grappling with critical challenges, including chronic diseases in aging populations, unprecedented health care staffing shortages and turnover, scarce resources, unprecedented demands and wait times, escalating healthcare expenditure, and declining health outcomes. As a result, policymakers and healthcare executives are investing in artificial intelligence (AI) solutions to increase operational efficiency, lower health care costs, and improve patient care. However, current level of investment in developing healthcare AI among members of the global digital health partnership does not seem to yield a high return yet. This is mainly due to underinvestment in the supporting infrastructure necessary to enable the successful implementation of AI. If a healthcare-specific AI winter is to be avoided, it is paramount that this disparity in the level of investment in the development of AI itself and in the development of the necessary supporting system components is evened out.

The recent success of Generative AI (GenAI) has heralded a new era in content creation, dissemination, and consumption. This technological revolution is reshaping our understanding of content, challenging traditional notions of authorship, and transforming the relationship between content producers and consumers. As we approach an increasingly AI-integrated world, examining the implications of this paradigm shift is crucial. This article explores the future of content in the age of GenAI, analysing the evolving definition of content, the transformations brought about by GenAI systems, and emerging models of content production and dissemination. By examining these aspects, we can gain valuable insights into the challenges and opportunities that lie ahead in the realm of content creation and consumption and, hopefully, manage them more successfully.

The international network of submarine cables plays a crucial role in facilitating global telecommunications connectivity, carrying over 99% of all internet traffic. However, submarine cables challenge digital sovereignty due to their ownership structure, cross-jurisdictional nature, and vulnerabilities to malicious actors. In this article, we assess these challenges, current policy initiatives designed to mitigate them, and the limitations of these initiatives. The nature of submarine cables curtails a state’s ability to regulate the infrastructure on which it relies, reduces its data security, and threatens its ability to provide telecommunication services. States currently address these challenges through regulatory controls over submarine cables and associated companies, investing in the development of additional cable infrastructure, and implementing physical protection measures for the cables themselves. Despite these efforts, the effectiveness of current mechanisms is hindered by significant obstacles arising from technical limitations and a lack of international coordination on regulation. We conclude by noting how these obstacles lead to gaps in states’ policies and point towards how they could be improved to create a proactive approach to submarine cable governance that defends states’ digital sovereignty.

The deployment of information and communication technologies (ICTs) and their uptake by society radically affect the human condition, insofar as it modifies our relationships to ourselves, to others and to the world. The ever-increasing pervasiveness of ICTs shakes established reference frameworks through the following transformations 1 : i. the blurring of the distinction between reality and virtuality; ii. the blurring of the distinctions between human, machine and nature; iii. the reversal from information scarcity to information abundance; and iv. the shift from the primacy of entities to the primacy of interactions.

The renaissance of epistemology between the two world wars forms a bridge between early modern and contemporary philosophy of knowledge. This paper traces the resurgence of interest in epistemology at the turn of the century, as a reaction against the nineteenth-century development of Neo-Kantian and Neo-Hegelian idealism, through the interwar renaissance of epistemology, prompted by major advances in mathematics, logic, and physics, and its ultimate transformation from a theory of ideas and judgement into a theory of propositional attitudes, sentences, and meanings.