Inizialmente, il saggio tratteggia una breve storia estetico-artistica della nuvola, dagli archè della filosofia naturalistica presocratica, in cui si mostrano le nuvole come un compendio perfetto della vita e della sua immagine mutevole, alla tesi kantiana sull’origine nuvolosa del cosmo. Le nuvole sono immagini attraverso le quali si evoca la struttura di cui è fatta ogni forma che ha in sé le leggi dell’apparire e della materia. Esse rappresentano, dunque, un’origine senza origine e il complesso rapporto analogico e linguistico con (...) la realtà. A partire da tali considerazioni lo scritto analizza per via analogica Equivalents (1993) dell’artista brasiliano Vik Muniz. Queste opere intensificano la riflessione sul vedere e sull’esistenza, mettono in discussione i principi della fotografia e aprono a nuove pratiche non più fotografiche ma forografiche. Le nuvole dimostrano come l’arte non sia il tentativo di cercare la bellezza ma di avere il coraggio di riceverla, sollecitando costantemente nuovi e mutevoli linguaggi. (shrink)

In una società in cui dilaga un’economia realista capitalista di tipo cultuale, indebitante e neoliberista, il principio di mercato è la principale fonte regolatrice dell’attività economica e relazionale umana. In questo orizzonte si produce una cultura dell’omologazione dove ogni attore tende a confermare la propria identità, che deve essere inserita in un sistema codificato per poter essere riconosciuta per ottenere in cambio un’utilità. Se il sistema realista capitalista si basa sul ʻcultoʼ individualista, per abbattere tale culto è necessario ricostruire una (...) collettività laica che tenti di ritrovare diversamente e coraggiosamente l’atto festivo col quale l’artista nell’antichità abitava una dimensione comune, partecipata e disinteressata. L’utilitarismo, l’egoismo, la paura, il cinismo venivano sospesi dall’atto di festa per poter contemplare nuovamente la vita in tutta la sua potente fragilità e liberare le prassi umane a lei collegate. (shrink)

Ecological-enactive approaches to cognition aim to explain cognition in terms of the dynamic coupling between agent and environment. Accordingly, cognition of one’s immediate environment (which is sometimes labeled “basic” cognition) depends on enaction and the picking up of affordances. However, ecological-enactive views supposedly fail to account for what is sometimes called “higher” cognition, i.e., cognition about potentially absent targets, which therefore can only be explained by postulating representational content. This challenge levelled against ecological-enactive approaches highlights a putative explanatory gap between (...) basic and higher cognition. In this paper, we examine scientific cognition—a paradigmatic case of higher cognition—and argue that it shares fundamental features with basic cognition, for enaction and affordance selection are central to the scientific enterprise. Our argument focuses on modeling, and on how models promote scientific understanding. We base our argument on a non-representational account of scientific understanding and on the material engagement theory, for models are hereby conceived as material objects designed for scientific engagements. Having done so, we conclude that the explanatory gap is significantly less threatening to the ecological-enactive approach than it might appear. (shrink)

The first essay is one of the most influential papers ever written on Lonergan; it and the second one inquire into the notion of the a priori. The third essay presents a detailed analysis of Kantian intuitionism and contrasts it with the 'knowledge as structure' position of Lonergan's critical realism. In this essay intuitionism is generalized, to allow Sala to address representatives of neoscholasticism as well. The argument with neoscholasticism continues in the fourth essay.

Assuming a radical stance on embodied cognition, according to which the information ac- quired through basic cognitive processes is not contentful (Hutto and Myin, 2013), and as- suming that perception is a source of rationally grounded knowledge (Pritchard, 2012), a pluralistic account of perceptual knowledge is developed. The paper explains: (i) how the varieties of perceptual knowledge fall under the same broader category; (ii) how they are subject to the same kind of normative constraints; (iii) why there could not be (...) a conflict between the different varieties of perceptual knowledge; and (iv) why the traditional episte- mological inquiry is inclined to overestimate the role of propositional perceptual knowledge. (shrink)

In this paper we argue that transparency of machine learning algorithms, just as explanation, can be defined at different levels of abstraction. We criticize recent attempts to identify the explanation of black box algorithms with making their decisions (post-hoc) interpretable, focusing our discussion on counterfactual explanations. These approaches to explanation simplify the real nature of the black boxes and risk misleading the public about the normative features of a model. We propose a new form of algorithmic transparency, that consists in (...) explaining algorithms as an intentional product, that serves a particular goal, or multiple goals (Daniel Dennet’s design stance) in a given domain of applicability, and that provides a measure of the extent to which such a goal is achieved, and evidence about the way that measure has been reached. We call such idea of algorithmic transparency “design publicity.” We argue that design publicity can be more easily linked with the justification of the use and of the design of the algorithm, and of each individual decision following from it. In comparison to post-hoc explanations of individual algorithmic decisions, design publicity meets a different demand (the demand for impersonal justification) of the explainee. Finally, we argue that when models that pursue justifiable goals (which may include fairness as avoidance of bias towards specific groups) to a justifiable degree are used consistently, the resulting decisions are all justified even if some of them are (unavoidably) based on incorrect predictions. For this argument, we rely on John Rawls’s idea of procedural justice applied to algorithms conceived as institutions. (shrink)

Computational systems are traditionally approached from control-oriented perspectives; however, as soon as we move from centralized to decentralized computational infrastructures, direct control needs to be replaced by distributed coordination mechanisms that are on par with institutional constructs observable in human societies (contracts, agreements, enforcement mechanisms, etc.). This paper presents a formalization of Hohfeld's framework building upon a logic whose language includes primitive operators of ability and parametric deontic operators. The proposal is meant to highlight the fundamental interaction between deontic and (...) potestative concepts and contains proofs of soundness and completeness with respect to a class of relational models. (shrink)

Real engines of the artificial intelligence revolution, machine learning models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In (...) this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs. (shrink)

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around (...) cardiopulmonary resuscitation and the determination of a patient’s Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions—conceptual, methodological and procedural—that need to be considered in further development and implementation efforts. (shrink)

In his recent article ‘Limits of trust in medical AI,’ Hatherley argues that, if we believe that the motivations that are usually recognised as relevant for interpersonal trust have to be applied to interactions between humans and medical artificial intelligence, then these systems do not appear to be the appropriate objects of trust. In this response, we argue that it is possible to discuss trust in medical artificial intelligence, if one refrains from simply assuming that trust describes human–human interactions. To (...) do so, we consider an account of trust that distinguishes trust from reliance in a way that is compatible with trusting non-human agents. In this account, to trust a medical AI is to rely on it with little monitoring and control of the elements that make it trustworthy. This attitude does not imply specific properties in the AI system that in fact only humans can have. This account of trust is applicable, in particular, to all cases where a physician relies on the medical AI predictions to support his or her decision making. (shrink)

dolce, the first top-level ontology to be axiomatized, has remained stable for twenty years and today is broadly used in a variety of domains. dolce is inspired by cognitive and linguistic considerations and aims to model a commonsense view of reality, like the one human beings exploit in everyday life in areas as diverse as socio-technical systems, manufacturing, financial transactions and cultural heritage. dolce clearly lists the ontological choices it is based upon, relies on philosophical principles, is richly formalized, and (...) is built according to well-established ontological methodologies, e.g. OntoClean. Because of these features, it has inspired most of the existing top-level ontologies and has been used to develop or improve standards and public domain resources. Being a foundational ontology, dolce is not directly concerned with domain knowledge. Its purpose is to provide the general categories and relations needed to give a coherent view of reality, to integrate domain knowledge, and to mediate across domains. In these 20 years dolce has shown that applied ontologies can be stable and that interoperability across reference and domain ontologies is a reality. This paper briefly introduces the ontology and shows how to use it on a few modeling cases. (shrink)

Artificial intelligence (AI) systems are quickly gaining ground in healthcare and clinical decision-making. However, it is still unclear in what way AI can or should support decision-making that is based on incapacitated patients’ values and goals of care, which often requires input from clinicians and loved ones. Although the use of algorithms to predict patients’ most likely preferred treatment has been discussed in the medical ethics literature, no example has been realised in clinical practice. This is due, arguably, to the (...) lack of a structured approach to the epistemological, ethical and pragmatic challenges arising from the design and use of such algorithms. The present paper offers a new perspective on the problem by suggesting that preference predicting AIs be viewed as sociotechnical systems with distinctive life-cycles. We explore how both known and novel challenges map onto the different stages of development, highlighting interdisciplinary strategies for their resolution. (shrink)

In this paper, I argue that knowledge is dimly luminous. That is: if a person knows that p, she knows how she knows that p. The argument depends on a safety-based account of propositional knowledge, which is salient in Williamson’s critique of the ‘KK’ principle. I combine that account with non-intellectualism about knowledge-how – according to which, if a person knows how to φ, then in nearly all (if not all) nearby possible worlds in which she φes in the same (...) way as in the actual world, she only φes successfully. Thus, the possession of first-order propositional knowledge implies secondorder practical knowledge, and this can be iterated. Because of the assumed nonintellectualism about know-how, dim luminosity does not imply bright luminosity about knowledge, which is expressed by the traditional KK principle. I conclude by considering some potential counterexamples to the view that knowledge is dimly luminous. (shrink)

ABSTRACT I propose a middle-ground between a perceptual model of self-knowledge, according to which the objects of self-awareness are accessed through some kind of causal mechanism, and a rationalist model, according to which self-knowledge is constituted by one's rational agency. Through an analogy with the role of the exercises of sensorimotor abilities in rationally grounded perceptual knowledge, self-knowledge is construed as an exercise of action-oriented and action-orienting abilities. This view satisfies the privileged access condition usually associated with self-knowledge without entailing (...) an insurmountable gap between self- knowledge and knowledge of other minds. RESUMO Proponho um meio-termo entre um modelo perceptual de autoconhecimento, segundo o qual os objetos de autoconhecimento são acessadas mediante um tipo de mecanismo causal, e um modelo racionalista, segundo o qual o autoconhecimento é constituído pela agência racional. Por analogia ao papel que o exercício de habilidades sensório-motoras desempenham no conhecimento perceptual racionalmente fundado, autoconhecimento é entendido como um exercício de habilidades que são orientadas pela e orientam a ação. Essa imagem satisfaz a condição de acesso privilegiado que geralmente é associada ao autoconhecimento sem implicar uma lacuna intransponível entre autoconhecimento e conhecimento de outras mentes. (shrink)

The aim of this paper is two-fold: first, it is intended to articulate theses that are often assessed independently, thus showing that a strong version of epistemological disjunctivism about perceptual knowledge implies a transformative conception of rationality. This entails that individuals in skeptical scenarios could not entertain rational thoughts about their environment, for they would fail to have perceptual states. The secondary aim is to show that this consequence is not a sufficient reason to abandon the variety of disjunctivism presented. (...) The argument for this claim depends on the assessment of rationality attributions to subjects in plausible cases of illusion and some clinical cases of hallucination. RESUMO Este artigo tem dois objetivos: primeiramente, pretende-se articular teses que são frequentemente avaliadas independentemente, mostrando com isso que uma versão robusta do disjuntivismo epistemológico sobre conhecimento perceptual implica uma concepção transformativa da racionalidade. Uma consequência disso é que indivíduos em cenários céticos não poderiam entreter pensamentos racionais sobre o ambiente em que habitam, pois eles não possuiriam estados perceptuais. Em segundo lugar, argumenta-se que a consequência delineada acima não é uma razão suficiente para rejeitar o disjuntivismo tal como apresentado. Esse argumento depende da avaliação de atribuições de racionalidade a indivíduos em casos plausíveis de ilusão e em alguns casos clínicos de alucinação. (shrink)

In this paper, I present a possible solution to the puzzle unveiled by Kornblith about the sources and the possibility of knowledge of epistemic norms. The puzzle is: if such norms cannot be discovered solely by reflection, and if there are correct ways of thinking and inferring, then such norms can only be discovered by investigating the world —a counterintuitive conclusion. To avoid skepticism about normativity, I argue that we create normative correctness and discover normative demands by investigating the world (...) and reflecting about our epistemic practices. This is done by an exposition of the method known as reflective equilibrium, which is defended against Kornblith’s thesis that the appeal to reflective equilibrium is doomed to failure because it implies reflection. (shrink)

Artificial intelligence has found its way into many areas of human life, serving a range of purposes. Sometimes AI tools are designed to help humans eliminate high-volume, tedious, routine tas...

Large language models (LLMs) have now entered the realm of medical ethics. In a recent study, Balaset alexamined the performance of GPT-4, a commercially available LLM, assessing its performance in generating responses to diverse medical ethics cases. Their findings reveal that GPT-4 demonstrates an ability to identify and articulate complex medical ethical issues, although its proficiency in encoding the depth of real-world ethical dilemmas remains an avenue for improvement. Investigating the integration of LLMs into medical ethics decision-making appears to be (...) an interesting avenue of research. However, despite the promising trajectory of LLM technology in medicine, it is crucial to exercise caution and refrain from attributing their expertise to medical ethics. Our thesis follows an examination of the nature of expertise and the epistemic limitations that affect LLM technology. As a result, we propose two more fitting applications of LLMs in medical ethics: first, as tools for mining electronic health records or scientific literature, thereby supplementing evidence for resolving medical ethics cases, and second, as educational platforms to foster ethical reflection and critical thinking skills among students and residents. The integration of LLMs in medical ethics, while promising, requires careful consideration of their epistemic limitations. Consequently, a well-considered definition of their role in ethically sensitive decision-making is crucial. (shrink)

The theory of collective agency and intentionality is a flourishing field of research, and our understanding of these phenomena has arguably increased greatly in recent years. Extant theories, however, are still ill-equipped to explain certain aspects of collective intentionality. In this article we draw attention to two such underappreciated aspects: the failure of the intentional states of collectives to supervene on the intentional states of their members, and the role of non-human factors in collective agency and intentionality. We propose a (...) theory of collective intentionality which builds on the ‘interpretationist’ tradition in metasemantics and the philosophy of mind as initiated by David Lewis and recently developed further by Robbie Williams. The collective-level analogue of interpretationism turns out to look different in some ways from the individual-level theory, but is well-suited to accommodating phenomena such as hybrid collective intentionality. Complemented with Kit Fine’s theory of variable embodiment, such a theory also provides a diachronic account of intentional collectives. (shrink)

The high predictive accuracy of contemporary machine learning-based AI systems has led some scholars to argue that, in certain cases, we should grant them epistemic expertise and authority over humans. This approach suggests that humans would have the epistemic obligation of relying on the predictions of a highly accurate AI system. Contrary to this view, in this work we claim that it is not possible to endow AI systems with a genuine account of epistemic expertise. In fact, relying on accounts (...) of expertise and authority from virtue epistemology, we show that epistemic expertise requires a relation with understanding that AI systems do not satisfy and intellectual abilities that these systems do not manifest. However, recent developments of large language models make us argue that in the future AI systems may endowed with a form of epistemic authority. Further, following the Distributed Cognition theory and adapting an account by Croce on the virtues of collective epistemic agents to the case of human-AI interactions we show that, if an AI system is successfully appropriated by a human agent, a hybrid epistemic agent emerges, which can become both an epistemic expert and an authority. Consequently, we claim that the aforementioned hybrid agent is the appropriate object of a discourse around trust in AI and the epistemic obligations that stem from its epistemic superiority. (shrink)

In our recent article ‘The Ethics of the Algorithmic Prediction of Goal of Care Preferences: From Theory to Practice’1, we aimed to ignite a critical discussion on why and how to design artificial intelligence (AI) systems assisting clinicians and next-of-kin by predicting goal of care preferences for incapacitated patients. Here, we would like to thank the commentators for their valuable responses to our work. We identified three core themes in their commentaries: (1) the risks of AI paternalism, (2) worries about (...) attacks to our humanity stemming from the use of AI and (3) the possibility of designing AI systems for more relevant use cases than the one we consider in our work. We shall focus on these themes, leaving aside some other interesting suggestions, given the limited space available for our response. Diaz Milian and Bhattacharyya discuss the risks of AI paternalism highlighting how the use of an AI to predict goal of care preferences may incentivise clinicians and surrogates to shift the burden of decision making to the machine.2 In particular, ‘[i]f the AI-generated response is given priority above the other agents’2 and the AI makes decisions autonomously, then ‘AI paternalism’ would emerge.2 Against this possibility, the authors suggest four safeguards to be implemented in the AI life-cycle.2 These are procedures that may improve the trustworthiness of the AI and human control over it.3 We agree that it is important …. (shrink)

In their article ‘Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI’, Durán and Jongsma discuss the epistemic and ethical challenges raised by black box algorithms in medical practice. The opacity of black box algorithms is an obstacle to the trustworthiness of their outcomes. Moreover, the use of opaque algorithms is not normatively justified in medical practice. The authors introduce a formalism, called computational reliabilism, which allows generating justified beliefs on the (...) algorithm reliability and trustworthy outcomes of artificial intelligence systems by means of epistemic warrants, called reliability indicators. However, they remark the need for reliability indicators specific to black box algorithms and that justified knowledge is not sufficient to justify normatively the actions of the physicians using medical AI systems. Therefore, Durán and Jongsma advocate for a more transparent design and implementation of black box algorithms, providing a series of recommendations to mitigate the epistemic and ethical challenges behind their use in medical practice. In this response, I argue that a peculiar form of black box algorithm transparency, called design publicity, may efficiently implement these recommendations. Design publicity encodes epistemic, that is, reliability indicators, and ethical recommendations for black box algorithms by means of four subtypes of transparency. These target the values and goals, their translation into design requirements, the performance and consistency of the algorithm altogether. I discuss design publicity applying it to a use case focused on the automated classification of skin lesions from medical images. (shrink)

In this paper we want to examine how the mutual understanding of speakers is reached during a conversation through collaborative processes, and what role is played by abductive inference (in the Peircean sense) in these processes. We do this by bringing together contributions coming from a variety of disciplines, such as logic, philosophy of language and psychology. When speakers are engaged in a conversation, they refer to a supposed common ground: every participant ascribes to the others some knowledge, belief, opinion (...) etc. on which to rely in order to reach mutual understanding. As the conversation unfolds, this common ground is continually corrected and reshaped by the interchanges. An abductive reasoning takes place, in a collaborative setting, in order to build new possible theories about the common ground. In reconstructing this process through the use of a working example, we argue that the integration of a collaborative perspective within the Peircean theory of abduction can help to solve some of the drawbacks that the critics of the latter have outlined, for example its permissivity and non generativity. (shrink)

Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. In fact LLMs, which are remarkably capable of simulating roles and personas, may lead (...) to mismatches between designers' intentions and users' perceptions of social attributes, risking to promote emotional manipulation and dangerous behaviors, cases of epistemic injustice, and unwarranted trust. To address these issues, we propose enhancing the ST framework with a fifth 'W-question' to clarify the specific social attributions assigned to LLMs by its designers and users. This addition aims to bridge the gap between LLM capabilities and user perceptions, promoting the ethically responsible development and use of LLM-based technology. (shrink)

Interventions in the human germline have been regarded as a red line in genetic engineering up to now. However, with the recent progress in genome editing techniques, first and foremost CRISPR-based methods, the tide seems to be turning. The therapeutic benefits in particular are brought forward as an argument in favor of germline genome editing. According to this view, the main benefit of GGE is disease prevention. The procedure could be used to prevent monogenetic conditions for which no treatment is (...) available so far. This leads many commentators to the view that the benefits of GGE outweigh the risks. In this article, I examine the status and ethical implications of GGE as a means of disease prevention. I intend to show that the notion of GGE as disease prevention is only true for one specific application. Instead of being preventive treatments, most applications of GGE are advanced techniques of assisted reproduction. As such, they can be thought of as wish-fulfilling medicine, that is the use of medical techniques for non-medically indicated purposes. Thus, it is necessary to reconsider the risk-benefit analysis of GGE for most of its applications. (shrink)