Luciano Floridi

On the standard positivist account, the criteria of legal validity are constituted by the practice of officials, but the interpretive competences that those officials exercise are acquired and reproduced largely through the scholarship on which jurists are trained. That scholarship is itself formed under specific conditions, like possessing tacit legal knowledge, belonging to an interpretative community, having a personal commitment to assertions for which an author answers, and reading each part of a text in light of the whole, and the whole in light of its parts. We argue that if scholars delegate to AI the very activities through which these competences are exercised and transmitted, the legal system risks circulating increasingly derivative representations of doctrine that ever fewer participants have learned to defend or recognize as sound.

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.

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 compare the unique characteristics of IMDs (including early-generation implantable BCIs) and emerging, “next-generation” implantable BCIs. We then use one case study in contemporary chip architecture—the SCALO (SCalable Architecture for LOw-power BCIs) computer system—to highlight distinctive features in the design and application of next-generation BCIs arising from these technologies, which necessitate reevaluating regulatory approaches. We identify critical ethical considerations for next-gen implantable BCIs, including unique conceptions of autonomy, identity, and mental privacy. Based on these insights, we suggest potential avenues for the ethical regulation of next-gen implantable BCIs, emphasizing the importance of interdisciplinary collaboration and proactive mitigation of potential harms. The goal is to support the responsible design and application of emerging BCIs, ensuring their safe and ethical integration into medical practice.

The dominant framework for analyzing artificial intelligence centeres on risk—the probability that systems will malfunction, be misused, or produce unintended consequences. We argue that this framing systematically obscures what matters most: the susceptibility of people, institutions, and infrastructures to harm. Using proceedings from the Yale Digital Ethics Center’s Digital Vulnerabilities in the Age of AI Summit (DIVAS), we advance a complementary reframing, from AI risk to AI-mediated vulnerability. We identify five mechanisms through which AI transforms existing vulnerabilities—speed, scale, scope, asymmetry, and opacity—and map their manifestation across four analytical levels: individual, relational, institutional, and infrastructural. The analysis reveals cross-cutting dynamics—a widening gap between technological dependency and democratic control, the limits of technosolutionism, and a persistent cat-and-mouse trap that frustrates technical countermeasures—that explain why interventions targeting a single level or domain consistently fall short. We defend three normative implications for governance: context-specific regulation matched to vulnerability dynamics, inversion of the burden of proof from affected parties to AI deployers, and investment in the social conditions—community institutions, knowledge ecosystems, trust relationships—that technosolutionist approaches neglect. AI governance cannot be approached as a purely technical exercise; it is ultimately a question of political power, burden allocation, and democratic accountability.

Floridi’s Certainty–Scope Conjecture holds that no artificial intelligence system can be simultaneously reliable on every input and broad enough to cover the full richness of unstructured data: reliability and breadth are in structural tension, and the tension is imposed by the finite information-processing capacity of any mechanism implementing intelligence. The Conjecture has until now been a philosophical thesis without a proof. This article supplies one, in the continuous regime in which the semantic targets that matter philosophically actually live. The article is the continuous half of a coordinated diptych whose discrete companion proves a matching Fano-type converse in the finite classification setting. The technical machinery is classical: taking certainty to be expected distortion under a grounded semantic target, scope to be rate–distortion complexity, and mechanism capacity to be an admissibility-constrained quantity, the main theorem establishes that every decoder must incur at least the capacity-imposed distortion floor. The structural content lies in the operational consequences. A matched-Gaussian reverse water-filling rule gives the optimal allocation of a fixed budget across heterogeneous experts; a multi-task distortion floor formalises the intuition that no system is excellent at everything at once; a side-information theorem bounds the lift obtainable from retrieval or tool use. Four critical responses to the original formulation—from Lissack, from Immediato (two articles), and from Watson and Sterkenburg—are addressed, with a worked statistical-learning instantiation in the Bayesian regime that aligns the framework structurally with the Russo–Zou/Xu–Raginsky mutual-information generalisation-bound literature. The rate–distortion formulation is shown to be invulnerable to the objections raised against earlier Kolmogorov-complexity-based formulations.

Much of the hype about AI exploits the fact that human cognition, language, and categorization rely on imaginative scaffolding. Some degree of metaphor and conceptual borrowing should not be seen as a methodological failure. Law does not meet AI as a blank slate. It reaches for metaphors and other imaginative patterns that make the unfamiliar legible, channeling attention toward certain features and away from others. These are not superficial rhetorical devices, but imaginative patterns that do real work: They frame risks, allocate authority, and steer doctrinal development. The paper uses cognitive linguistics to trace how imaginative patterns structure the regulatory agenda and create path dependence. It concentrates on three especially influential conceptions that circulate across technical, legal, and public discourse: (artificial) intelligence, black box, and hallucinations. “Intelligence” invites personification and prototype effects that lead to misplaced assumptions about agency, even though what is actually being produced is best described as a simulation of a much narrower capacity associated with intelligence: linguistic competence. “Black box” turns a distributed sociotechnical system into a metaphoric bounded container, misleadingly focusing concerns about transparency and responsibility on the model abstracted from its development and implementation. “Hallucinations” imports from psycho-pathology an inappropriate frame which obscures the fact that many errors are foreseeable products of intentional choices. We suggest, instead, reframing them as deferential hazards that arise from design features and expected interactional dynamics.

Publishing has addressed the challenges posed by large language models (LLMs) through a provenance strategy: detectors, declarations, watermarks, and attestations. Provenance matters, but as a test of authorship or quality, it targets the wrong question, is unreliable for that purpose, and risks penalising non-native English writers through a ‘style tax’ on academic prose. Even a dependable instrument would indicate only that a sequence came from a specific distribution, not that the claims are reliable or that the author is accountable. The function publishing has always tracked is answerability – the standing to answer for the claims made – a standing no AI system is eligible to hold, and one current policy already invokes when it refuses AI authorship. Thus, in this article, I argue in favour of a role-indexed architecture specifying what answerability requires of authors, reviewers, editors, and publishers, and where editorial LLMs (eLLMs) belong within it. eLLMs sit in the workflow as recommenders, with calibrated reliance, subject to auditing, appeals, and overrides, and their deployment justified by field-specific evidence, making editors answerable as well. The shift is one of rigour, not of principle.

We argue that generative AI can degrade research by eroding the very practices through which scholarly judgement is formed and academic trust is built. As constitutive conditions for the production and validation of knowledge, these practices cannot be reduced to the final outputs of research, which is what AI so effectively simulate. Accordingly, when researchers delegate central tasks of inquiry to systems like Large Language Models (LLMs), they may stop enacting these practices and, with them, lose access to the formation they provide. An individual research output generated by AI may even appear improved but the researcher behind it fails to develop. Against this risk, merely keeping humans "in the loop" as prompters or quality-checkers of AI outputs is insufficient to preserve research as a site of intellectual formation. What is needed instead is a renewed commitment to research as a lived practice in which judgement is formed gradually, often through frictions, and participation in a scholarly community. We defend it because it rests on four sources and warrants of research that cannot be automated: tacit knowledge, personal commitment, socialisation, and deep reading. This practice enacts what we call ‘second scholarship’, by which we understand the re-appropriation of scholarly craft, chosen out of a critical experience of what generative AI can and cannot do. What cannot and should not be delegated becomes what research communities must value and answer for. This is what is left for us.

Large language models (LLMs) produce fluent, persuasive answers even when the interaction provides no adequate reason to accept them. The risk falls on the human side: overcommitment to claims that the exchange does not support. To assess it, we propose Turing's mirror, a structural inversion of the Turing test. Where the Turing test fixes a restricted dialogue and asks whether a machine can pass for a human, Turing's mirror keeps the same roles, dialogue, and evaluation but reverses the target: the human subject is the examinee, the LLM becomes part of the apparatus, and the question is whether the subject's expressed commitment tracks the warrant that the interaction makes available. Unlike adjacent work on automation bias and appropriate reliance, which records whether people accept or reject suggestions, this inversion makes the calibration of commitment to available warrant the measured quantity, a target we call protocol-relative warranted commitment. We distinguish settings in which suspension of judgment is responsible from settings in which some commitment must be expressed under uncertainty, and we introduce a light formal schema that separates truth in the world from what a protocol makes decisively settleable. On that basis, we specify a family of trial types, a graded report over support for truth, support for falsity, and unresolved commitment, and a limited role for proper scoring. Worked examples show why a subject may be correct without being warranted, and cautious without thereby failing. The result is a disciplined starting point for studying warrant-sensitive reliance on fluent LLM output.

Political parties strive to understand their electorates, and focus groups are a vital tool in these efforts. AI-enhanced simulation technologies (AESTs) enable synthetic focus groups in a fraction of the time (and cost), raising the question of when and how such simulated evidence can be used in campaign research. This paper develops a decision matrix to help party strategists match research needs to appropriate simulation technologies and to identify when to escalate to hybrid or fully human focus groups. The matrix combines three dimensions: strategic purpose, deployment risk, and empirical grounding of the simulation tool. Strategic purpose is the decisive dimension, as it determines what kind of evidence the focus group is meant to produce: observing how political meanings and identities emerge through interaction (Mode 1) or testing and refining campaign messages (Mode 2). The matrix shows that, given documented failure modes such as sycophancy, persona drift, and the suppression of minority viewpoints, AESTs cannot replace human interaction in Mode 1 at any risk level. Within Mode 2, suitability depends instead on deployment risk and on the empirical grounding. Yet even here, we caution that routine reliance on AESTs may erode the qualitative craft on which sound judgment depends.

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.

This article elaborates and defends philosophical microhistory—the intensive study of small episodes such as quotes, marginal anecdotes, editorial decisions, or forgotten glosses—as an undertheorised approach to the historiography of philosophy. Adapting Italian microhistory, it treats ideas as semantic capital circulating through networks of thinkers, translators, and institutions. The article presents a framework centred on levels of abstraction, showing how microscopic and macroscopic scales function as complementary perspectives. It contextualises philosophical microhistory relative to Lovejoy's history of ideas, rational reconstruction, Cambridge contextualism, Begriffsgeschichte, reception history, and Foucauldian genealogy, arguing that the approach supplements rather than supplants existing methods. It addresses objections concerning antiquarianism, representativeness, and the micro–macro problem, concluding that digital humanities and global intellectual history present new opportunities. Philosophical microhistory emerges as a methodology valuing contingency, foregrounding marginal voices, and disclosing how philosophy's most significant topics may sometimes be shaped by its most minor details.

The emergence of Agentic Artificial Intelligence (AAI) systems capable of independently initiating digital interactions requires a new optimisation paradigm designed explicitly for seamless agent-platform interactions. This article introduces Agentic AI Optimisation (AAIO) as an essential methodology for ensuring effective integration between websites and agentic AI systems. As Search Engine Optimisation (SEO) has shaped digital content discoverability, AAIO can define interactions between autonomous AI agents and online platforms. By examining the mutual interdependence between website optimisation and agentic AI success, the article highlights the virtuous cycle that AAIO can create. It further explores the governance, ethical, legal, and social implications (GELSI) of AAIO, emphasising the need for proactive regulatory frameworks to mitigate potential negative impacts. The article concludes by affirming AAIO’s essential role as part of a fundamental digital infrastructure in the era of autonomous digital agents, advocating equitable and inclusive access to its benefits.

Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest-profile sets of ethical principles for AI. We assess whether these principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principles for ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts.

The international governance of artificial intelligence (AI) is often said to suffer from a regulatory gap. In this article, I argue that the opposite problem is equally significant. The governance field is characterised by institutional proliferation and normative inflation: an excess of principles, declarations, and advisory bodies that lack binding authority or hierarchical coordination. This 'crying wolf' dynamic results from repeated, high-urgency normative signalling that, without enforcement mechanisms, gradually diminishes the credibility, relevance, and guiding power of the norms themselves. I distinguish between iterative soft law-which advances towards binding instruments through successive approximation-and substitutive soft law, which replaces enforcement with expression. I contend that this dynamic poses a structural threat to the performative credibility of technology governance and represents an ethical failure, as it gives everyone, particularly those most vulnerable to AI's harms, the false illusion of protection while offering none. The challenge facing the international governance of AI is to cease generating more empty signals and to strengthen institutional influence through delegated authority, normative and epistemic integration, and credible consequences. I conclude by proposing three design criteria that any future consolidation effort must meet, and by asserting that the ethics of governance architecture and its design deserve as much philosophical scrutiny as the ethics of the technologies being developed and overseen.

In this paper, we examine the potential integration of artificial intelligence (AI) into capital punishment through lethal autonomous mechanisms (LAMs), an extension of developments in lethal autonomous weapons (LAWs). While most philosophical and ethical research on autonomous weapons systems focuses on warfare and policing, their potential role in capital executions remains unexplored. Through anticipatory analysis of LAMs’ integration into what we term the “death row network,” we show how retentionists might leverage these technologies to counter key abolitionist critiques. We analyze how LAMs could undermine two abolitionist arguments: the harm to corrections personnel and the medicalization of execution protocols. However, we argue that while LAMs resolve these specific challenges, they simultaneously generate new ethical concerns. First, LAMs will not eliminate the moral and psychological burden on corrections personnel entirely but rather redistribute it to networks of AI developers, software engineers, and technology professionals who become implicated in state-sanctioned killings through their work. Similarly, LAMs address the medicalization concern, yet they merely shift ethical scrutiny from the medical sector to the technology corporations developing LAMs and their industrial partners. Second, LAMs’ elimination of human presence undermines the exercise of benevolence through sustained interpersonal engagement, from sharing final conversations to offering comfort during an inmate’s last moments. This human connection, which preserves dignity even within a dehumanizing process, cannot be replicated by automated systems and is a further argument against their adoption.

The article analyses the integration of Large Language Model (LLM)-based interfaces (editorial LLMs or eLLMs) in scholarly publishing workflows, focusing specifically on their growing role in editorial screening, manuscript preparation, and peer-review processes. It assesses the benefits eLLMs offer, including efficiency gains, improved compliance with journal guidelines, enhanced objectivity, and reduced editorial workload; and the risks, especially algorithmic biases, false positives and negatives, data privacy concerns, and potential opacity in automated decision-making. The article then offers some design recommendations for eLLMs that prioritise transparency, fairness, and user-centredness, ensuring human oversight remains integral to the editorial process. It concludes by encouraging a proactive and thoughtful engagement with these technologies to enhance scholarly publishing rather than undermine its values.

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.

Purpose – To review and discuss Luciano Floridi’s 2019 book The Logic of Information: A Theory of Philosophy as Conceptual Design, the latest instalment in his philosophy of information (PI) tetralogy, particularly with respect to its implications for library and information studies (LIS). Design/methodology/approach – Nine scholars with research interests in philosophy and LIS read and responded to the book, raising critical and heuristic questions in the spirit of scholarly dialogue. Floridi responded to these questions. Findings – Floridi’s PI, including this latest publication, is of interest to LIS scholars, and much insight can be gained by exploring this connection. It seems also that LIS has the potential to contribute to PI’s further development in some respects. Research implications – Floridi’s PI work is technical philosophy for which many LIS scholars do not have the training or patience to engage with, yet doing so is rewarding. This suggests a role for translational work between philosophy and LIS. Originality/value – The book symposium format, not yet seen in LIS, provides forum for sustained, multifaceted and generative dialogue around ideas.

Much of the hype about AI exploits the fact that human cognition, language, and categorization rely on imaginative scaffolding. Some degree of metaphor and conceptual borrowing should not be seen as a methodological failure. Law does not meet AI as a blank slate. It reaches for metaphors and other imaginative patterns that make the unfamiliar legible, channeling attention toward certain features and away from others. These are not superficial rhetorical devices, but imaginative patterns that do real work: They frame risks, allocate authority, and steer doctrinal development. The paper uses cognitive linguistics to trace how imaginative patterns structure the regulatory agenda and create path dependence. It concentrates on three especially influential conceptions that circulate across technical, legal, and public discourse: (artificial) intelligence, black box, and hallucinations. “Intelligence” invites personification and prototype effects that lead to misplaced assumptions about agency, even though what is actually being produced is best described as a simulation of a much narrower capacity associated with intelligence: linguistic competence. “Black box” turns a distributed sociotechnical system into a metaphoric bounded container, misleadingly focusing concerns about transparency and responsibility on the model abstracted from its development and implementation. “Hallucinations” imports from psycho-pathology an inappropriate frame which obscures the fact that many errors are foreseeable products of intentional choices. We suggest, instead, reframing them as deferential hazards that arise from design features and expected interactional dynamics.

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.

Biological computing (biocomputing) leverages biologically derived materials and processes, such as DNA and protein synthesis, to perform computational tasks. Biocomputing offers significant advantages over traditional silicon-based systems in terms of scalability, energy efficiency, computational flexibility, and information storage potential. However, the distinct operational characteristics of biocomputing raise novel governance, ethical, legal, and social implications (GELSI). This article identifies and analyzes key GELSI concerns raised by biocomputing. It then highlights the inadequacies of current regulatory frameworks in addressing the unique challenges posed by massively parallel molecular computations, biosafety risks, probabilistic error management in clinical applications, patentability of biological storage systems, and ownership rights in self-replicating data systems. The analysis concludes by recommending specialized regulatory approaches and international collaboration to govern biocomputing technologies responsibly, ensuring their ethical integration and equitable benefits across global societies.

In this paper, we examine the potential integration of artificial intelligence (AI) into capital punishment through lethal autonomous mechanisms (LAMs), an extension of developments in lethal autonomous weapons (LAWs). While most philosophical and ethical research on autonomous weapons systems focuses on warfare and policing, their potential role in capital executions remains unexplored. Through anticipatory analysis of LAMs’ integration into what we term the “death row network,” we show how retentionists might leverage these technologies to counter key abolitionist critiques. We analyze how LAMs could undermine two abolitionist arguments: the harm to corrections personnel and the medicalization of execution protocols. However, we argue that while LAMs resolve these specific challenges, they simultaneously generate new ethical concerns. First, LAMs will not eliminate the moral and psychological burden on corrections personnel entirely but rather redistribute it to networks of AI developers, software engineers, and technology professionals who become implicated in state-sanctioned killings through their work. Similarly, LAMs address the medicalization concern, yet they merely shift ethical scrutiny from the medical sector to the technology corporations developing LAMs and their industrial partners. Second, LAMs’ elimination of human presence undermines the exercise of benevolence through sustained interpersonal engagement, from sharing final conversations to offering comfort during an inmate’s last moments. This human connection, which preserves dignity even within a dehumanizing process, cannot be replicated by automated systems and is a further argument against their adoption.

This article examines the nature of reasoning in current, mainstream Large Language Models (LLMs) that operate within the token-completion paradigm. We explore their stochastic foundations and phenomenological resemblance to human abductive reasoning. We argue that such LLMs generate text based on learned associations rather than performing abductive inferences. When their output exhibits an apparent abductive quality-often reinforced by interface design-this effect is due to the model's training on human-generated texts that encode reasoning structures. We use some examples to illustrate how LLMs produce plausible hypotheses, simulate commonsense reasoning, and provide explanatory answers without grounding them directly in truth, semantics, verification, or understanding, and without any abductive reasoning. This duality, centred on the stochastic core of the models and the abductive appearance of the applications, has important implications for the evaluation and use of LLMs. They can help generate hypotheses and support human reasoning, but their outputs must be critically examined because they cannot discern truth or verify explanations. In the conclusion, we address five objections to our assertions, some limitations of our analysis, and provide a general assessment.

_Philosophy and Computing_ explores each of the following areas of technology: the digital revolution; the computer; the Internet and the Web; CD-ROMs and Mulitmedia; databases, textbases, and hypertexts; Artificial Intelligence; the future of computing. Luciano Floridi shows us how the relationship between philosophy and computing provokes a wide range of philosophical questions: is there a philosophy of information? What can be achieved by a classic computer? How can we define complexity? What are the limits of quantam computers? Is the Internet an intellectual space or a polluted environment? What is the paradox in the Strong Artificial Intlligence program? _Philosophy and Computing_ is essential reading for anyone wishing to fully understand both the development and history of information and communication technology as well as the philosophical issues it ultimately raises.

This article advances three interconnected theses concerning the philosophical, ethical, and socio-political implications of the digital and analogue relationship. The first thesis argues that digital representations (models) increasingly mediate our cognitive and practical engagement with reality (systems), resulting in epistemic abstraction and potential oversights (“the eclipse of the analogue”). Through examples from healthcare, engineering, and finance, the article illustrates how uncritical dependence on models can obscure crucial aspects of analogue realities, leading to significant errors and adverse consequences. The second thesis contends that, at the same time and only apparently paradoxically, analogue factors—such as rare earth minerals, energy infrastructures, and physical hardware—are becoming central to the control and governance of digital technologies (“the hardware turn”). Despite digitalisation’s rhetoric of immateriality and virtuality, power increasingly resides with those who control the analogue conditions of possibility underpinning the digital domain. Consequently, the third thesis proposes a normative framework for addressing this twofold challenge. The article argues for the necessity of three complementary strategies: enhanced education (Paideia) to foster critical awareness of digital mediation; robust legislation (Nomos) to ensure ethical governance and mitigate systemic risks; and a redefined conception of digital sovereignty capable of democratically controlling the interconnected digital–analogue landscape and implement the appropriate Paideia and the right Nomos. In the end, Heidegger’s metaphysical critique of technology is contrasted with Weil’s ethically oriented approach, advocating a constructive and relational philosophy of technology to counter the detrimental impacts of the eclipse of the analogue and the hardware turn.

Debates on AI governance often focus on regulating risks. This article shifts perspective to examine how AI can augment democratic processes, presenting a critical analysis of the Italian Chamber of Deputies’ pioneering AI initiative. We detail the 2024 project that produced three prototype systems—NORMA (legislative analysis), MSE (drafting assistance), and DepuChat (citizen engagement)—which embed principles of transparency, human oversight, and privacy-by-design. We introduce the project’s third way development model, a public-academic partnership that contrasts with full in-house or commercial approaches. Using this initiative as a critical case study, we move beyond merely applying the concept of “augmented democracy”. We argue that this real-world implementation reveals key tensions between the goals of efficiency and the preservation of deliberative friction essential to democratic practice. The analysis highlights risks, including staff deskilling and the digitalisation of inequalities, and situates the Italian approach in an international context. We conclude by offering a theoretical refinement of augmented democracy, informed by the practical lessons of its implementation in a complex legislative environment.