Daniele Chiffi

Landslide risk offers a particularly interesting field of study for philosophers of science working on causation. Landslides are the result of complex causal dynamics, and the object of study of a variety of disciplines which are ultimately aimed at effective risk mitigation. For these reasons, causation is central to many instruments developed to study landslides. We illustrate four such instruments, and we show how each of them assumes a different concept of causation. This observation provides the ground for the defence of a pluralist account of conceptual causation. However, in defending this position we do not aim at establishing a universally correct account of causation. Instead, we examine the adequacy of different concepts for a specific purpose and in a specific context. We therefore supplement the descriptive observation of the plurality of concepts of causation involved in landslide research with the normative claim that this plurality cannot be reduced under a monist account, because the different concepts are necessary for different epistemic purposes. While our conclusions only hold for the specific case we analyse, the instrumentalist approach we propose can be generalised to other contexts and to other concepts.

Scientific evidence and scientific values under risk and uncertainty are strictly connected from the point of view of Peirce’s pragmaticism. In addition, economy and statistics play a key role in both choosing and testing hypotheses. Hence we may show also the connection between the methodology of the economy of research and statistical frequentism, both originating from pragmaticism. The connection is drawn by the regulative principles of synechism, tychism and uberty. These principles are values that have both epistemic and non-epistemic dimension. They concern both the decisions to test a hypothesis as well as inductive risk. The validity of this result stems from the values cost–benefit analysis imposes on scientific inquiry. Values associated with the economy of research are important not only in the pre-test phases of generating hypotheses but also when hypotheses are effectively tested. Peirce took these economic considerations to leave room for an interpretation of probability which is not only a frequentist and propensity-theoretic but also a conceptualist one referring to degrees of belief. We show that this leeway nonetheless agrees with the theory of the economy of scientific methods.

The present paper is intended to analyse from a theoretical point of view the relationships between natural language and idiolects in the context of communication by means of the Davidson–Dummett controversy on the nature of language. I will explore from a pragmatic point of view the reliability of an alternative position inspired by the recent literalism/contextualism debate in philosophy of language in order to overcome some limitations of Dummett’s and Davidson’s perspectives on language, idiolects and communication.

This pioneering book investigates the complex nature of urban systems, exploring how uncertainty plays a crucial role in understanding cities. Daniele Chiffi and Stefano Moroni develop a relational theory to examine cities as dynamic networks of interaction between people and objects, rather than as fixed structures. Chiffi and Moroni analyse how uncertainty affects urban planning, infrastructure and technological advancement, challenging conventional paradigms by demonstrating how uncertainty can spark innovation. They further assess how public authorities make decisions in complicated urban contexts, focusing on areas such as land use and building transformations. The book proposes a novel framework for understanding technological innovations in the city, emphasising that progress is not solely driven by economic factors, but also by societal preferences and underlying uncertainties. Providing fresh insights into the epistemology of urban systems, this book is an inspiring read for students and scholars of urban planning, urban geography and cities more broadly. It is also a useful resource for policymakers and practitioners in planning, as well as academics interested in the philosophy of urban studies.

Abductive reasoning is a multifaceted process, encompassing various perspectives, models, and logics, which can make it sometimes quite intricate to understand. Lorenzo Magnani’s work has been invaluable in providing important distinctions that shed light on relevant aspects of abductive reasoning that I analytically discuss. This chapter mainly investigates both the Peircean account of abduction and the methodology of inference to the best explanation, elucidating the role of cost-benefit analysis within Peirce’s views on the economy of research as well the role of multicriteria analysis within the framework of inference to the best explanation. Then, I introduce a new distinction between strong and weak hypotheses and analysed them in their roles in the context of abduction and inference to the best explanation, respectively. A weak hypothesis is considered justified in the absence of complete evidence against it (given a specific knowledge base), transforming into a conjecture upon a successful abduction. A strong hypothesis is justified by its greater probability compared to the hypothesis of its negated content (given the same knowledge base), aligning with the conservatism criterion in inference to the best explanation. The discussion concludes with the potential and challenges of achieving genuine creative abductions through generative AI systems.

Over the past few years, mobile health applications (mHealth apps) have gained popularity regarding the management of chronic conditions, such as diabetes, as they are considered to enhance follow‐up and treatment. Indeed, these applications are powerful tools that support individualised pharmaceutical and non‐pharmaceutical care by remotely monitoring the patient's health status in real‐time. Nevertheless, concerns have been raised regarding trust and trustworthiness towards their use, in particular, when they are powered by Artificial Intelligence (AI). Trust in AI is a multifaceted notion that encompasses aspects such as reliance, risk, as well as ethical principles (e.g., respect for human autonomy and prevention of harm). In this paper, we provide an analysis of trust and trustworthiness in AI‐powered mHealth apps for diabetes through the lens of philosophy and risk analysis to promote the patient's well‐being.

The notion of Trustworthy Artificial Intelligence (TAI) has increasingly gained prominence in discussions on the ethical deployment of AI systems, and it is playing a foundational role in the European strategy for regulating AI. However, doubts have been raised about the applicability of the notions of trust and trustworthiness to AI systems. In particular, it has been argued that trust is inherently tied to paradigmatically human features, which AI systems lack. Our aim here is twofold. First, we argue that this objection is grounded in two separate unwarranted assumptions: one concerning the precise relationship between TAI and interpersonal trust, and the other regarding the literal use of trust in AI. Second, we show that by relinquishing either of these assumptions, there are two distinct ways to address the objection, enabling a sound and meaningful ascription of trust within the context of AI systems.

AI-powered chatbots are increasingly used in many contexts and for a variety of purposes. Among these uses, a particularly interesting one involves the so-called griefbots – that is, chatbots impersonating dead persons in the form of an artificial interlocutor. While they might help us process the loss of a beloved person, griefbots are not free from risks and may give rise to ethical concerns. This work aims at expanding the existing philosophical debate on griefbots. After providing a brief introduction to griefbots as well as the philosophical debate on their use, we provide an analysis of the way they may give rise to problematic ethical and normative tension between different transparency requirements. Then, we emphasise how the assessment of the related risks should adequately account for the dimension of users’ vulnerability.

The Frege-Geach problem is a central issue in metaethics, challenging expressivist theories to justify logical inferences involving moral expressions. Expressivists argue that moral statements express attitudes rather than truth-apt propositions, yet this position struggles with preserving logical coherence in contexts where moral claims are unasserted. Solutions to this problem include Simon Blackburn’s approach involving higher-order attitudes, Mark Schroeder’s ”being for” framework, and Allan Gibbard’s theory of factual-normative worlds. Each framework contributes insights, yet a comprehensive resolution may necessitate combining linguistic and pragmatic analyses with metaethical perspectives to clarify inference patterns in expressivist reasoning.

The paper proposes two logical analyses of (the norms of) justification. In a first, realist-minded case, truth is logically independent from justification and leads to a pragmatic logic LP including two epistemic and pragmatic operators, namely, assertion and hypothesis. In a second, antirealist-minded case, truth is not logically independent from justification and results in two logical systems of information and justification: AR4 and AR4¢, respectively, provided with a question-answer semantics. The latter proposes many more epistemic agents, each corresponding to a wide variety of epistemic norms. After comparing the different norms of justification involved in these logical systems, two hexagons expressing Aristotelian relations of opposition will be gathered in order to clarify how (a fragment of) pragmatic formulas can be interpreted in a fuzzy-based question-answer semantics.

I In this paper, we investigate the nature of empirical hypotheses used in scientific reasoning and the act of formulating hypotheses. This is achieved through a novel logical framework in which we provide specific semantics for two types of hypotheses: a strong and a weak sense of hypothesis, each characterized by different logical structures. This framework enables us to better characterize certain aspects of hypothetical reasoning in scientific practice, especially when we attempt to rationally deny the content of an empirical hypothesis.

This paper addresses the relatively overlooked field of rehabilitation and physical medicine, offering an epistemological perspective on clinical reasoning in these disciplines, focusing on three different domains: diagnosis, prognosis, and treatment. Rehabilitation sciences, often overshadowed by medicine and nursing, present unique challenges in terms of clinical reasoning. We explore these challenges, highlighting the distinctive features that set rehabilitation apart from clinical medicine. Notably, rehabilitation focuses on functions, aiming to improve an individual’s quality of life, setting it apart from disease-centered medicine. Drawing also from philosophical insights in nursing literature, we offer a multidisciplinary perspective on the epistemological dimensions of rehabilitation and physical medicine, shedding light on their placement within healthcare disciplines.

Recent logic and linguistic literature suggest that certain forms of denial cannot be reduced to a simple assertion of negation. In particular, the existence of mathematical and empirical conjectures offers a basis for refuting Frege’s thesis of equivalence between denial and assertion of negation. Following this line of thought we develop a formal framework with two primitive illocutionary operators for assertion and denial, where denial is not a simple negation of assertion. We introduce a semantics for the operators mentioned above. Finally, we discuss the relations between asserted and denied contents.

Risks connected with AI systems have become a recurrent topic in public and academic debates, and the European proposal for the AI Act explicitly adopts a risk-based tiered approach that associates different levels of regulation with different levels of risk. However, a comprehensive and general framework to think about AI-related risk is still lacking. In this work, we aim to provide an epistemological analysis of such risk building upon the existing literature on disaster risk analysis and reduction. We show how a multi-component analysis of risk, that distinguishes between the dimensions of hazard, exposure, and vulnerability, allows us to better understand the sources of AI-related risks and effectively intervene to mitigate them. This multi-component analysis also turns out to be particularly useful in the case of general-purpose and experimental AI systems, for which it is often hard to perform both ex-ante and ex-post risk analyses.

The Covid-19 pandemic has been analysed and discussed from many disciplinary perspectives. An aspect that still needs critical exploration is the role—that is, the modes and forms—of regulatory interventions during the pan- demic. It is interesting to note in this regard that, in many studies, regulatory measures are labelled “non-pharma- ceutical interventions”, as if they do not have any specificity on their own and only represent a theoretically residual category. The main aim of this article is instead to focus on the distinctive features of normative measures as such. As regards the article’s focus, it centres on the normative interventions in the first period of the pandemic—that is, 2020—with particular reference to Italy. We have chosen to focus on this period because the most extensive and severe restrictions introduced to combat Covid-19 were established at that time. And we have chosen to con- sider Italy because it was the first country, after China, to be hard hit by the virus and to react in a draconian manner. As regards the methodology, the article is based, in general and primarily, on an extensive interdisciplinary literature review. With reference to the Italian case, the study is additionally based on: first-hand data collection and analysis (especially concerning Italian normative measures mainly issued at the national level: their type, frequency, target, hierarchical relationships); second-hand data and analysis (for instance as regards the effectiveness of Italy’s and other countries’ regulatory measures). In discussing the Italian response to the Covid-19 pandemic, certain weaknesses have been identified; and possible lessons have been highlighted, in terms of both “planning” (i.e. the necessity to better pre-define concrete and circumscribed sets of actions) and of “meta-planning” (i.e. the necessity of better planning the way in which public authorities could and should plan and act). Although this article is mainly based on the Italian situation, what we can learn from this case is largely generalizable.

In Die Grundgesetze der Arithmetik (I, §48), Frege introduces his rule of the fusion of horizontals, according to which if an occurrence of the horizontal stroke is followed by another occurrence of the same stroke, either in isolation or “contained” in a propositional connective, the two occurrences can be fused with each other. However, the role of this rule, and of the horizontal sign more generally, is controversial; Michael Dummett notoriously claimed, for instance, that the horizontal is “wholly superfluous” in Frege's logical system. In this paper, we challenge Dummett's view by providing a comprehensive analysis of the significance of the horizontal stroke. After some preliminary remarks, we argue that even if Frege's connectives in some sense “contain” the horizontal, yet they are total functions. Then, we take up the question of the sense expressed by the horizontal, and we claim that, unlike other sentential operators, the horizontal is not sense‐compositional. Finally, we consider the semantic and pragmatic aspects of Frege's horizontal in connection to his judgment stroke and the double judgment stroke. Contra Dummett, we argue that the horizontal is a special and indispensable element of Frege's logic.

A lot of attention has recently been devoted to the notion of Trustworthy AI (TAI). However, the very applicability of the notions of trust and trustworthiness to AI systems has been called into question. A purely epistemic account of trust can hardly ground the distinction between trustworthy and merely reliable AI, while it has been argued that insisting on the importance of the trustee’s motivations and goodwill makes the notion of TAI a categorical error. After providing an overview of the debate, we contend that the prevailing views on trust and AI fail to account for the ethically relevant and value-laden aspects of the design and use of AI systems, and we propose an understanding of the notion of TAI that explicitly aims at capturing these aspects. The problems involved in applying trust and trustworthiness to AI systems are overcome by keeping apart trust in AI systems and interpersonal trust. These notions share a conceptual core but should be treated as distinct ones.

The importance of epistemic values in science is universally recognized, whereas the role of non-epistemic values is sometimes considered disputable. It has often been argued that non-epistemic values are more relevant in applied sciences, where the goals are often practical and not merely scientific. In this paper, we present a case study concerning earthquake engineering. So far, the philosophical literature has considered various branches of engineering, but very rarely earthquake engineering. We claim that the assessment of seismic hazard models is sensitive to both epistemic and non-epistemic values. In particular, we argue that the selection and evaluation of these models are justified by epistemic values, even if they may be contingently influenced by non-epistemic values. By contrast, the aggregation of different models into an ensemble is justified by non-epistemic values, even if epistemic values may play an instrumental role in the attainment of these non-epistemic values. A careful consideration of the different epistemic and non-epistemic values at play in the choice of seismic hazard models is thus practically important when alternative models are available and there is uncertainty in the scientific community about which model should be used.

The goal of this paper is to review and critically discuss the philosophical aspects of probabilistic seismic hazard analysis (PSHA). Given that estimates of seismic hazard are typically riddled with uncertainty, diferent epistemic values (related to the pursuit of scientifc knowledge) compete in the selection of seismic hazard models, in a context infuenced by non-epistemic values (related to practical goals and aims) as well. We frst distinguish between the diferent types of uncertainty in PSHA. We claim that epistemic and nonepistemic considerations are closely related in the selection of the appropriate estimate of seismic hazard by the experts. Finally, we argue that the division of scientifc responsibility among the experts can lead to responsibility gaps. This raises a problem for the ownership of the results (“no one’s model” problem) similar to the “problem of many hands” in the ethics of technology. We conclude with a plea for a close collaboration between philosophy and engineering.

The concept of autonomy is crucial for the theoretical characterization of robots and, more in general, complex technological artifacts. The aim of this paper is to provide a conceptual and logical framework in which it is possible to define two concepts of autonomy: autonomy of performance and autonomy of process. The analysis is carried out exploiting the logical resources of the counterfactual semantics-developed by Lewis' and Stalnaker's seminal works-and branching structures of the possible courses of actions. It allows to differentiate the autonomy of a robotic arm from the autonomy of a highly complex machine-like rovers for the explorations of the planets. The clarification of the concept of autonomy is, moreover, an essential precondition for the investigation concerning the ethics of artificial agents.

Technological innovation is almost always investigated from an economic perspective; with few exceptions, the specific technological and social nature of innovation is often ignored. We argue that a novel way to characterise and make sense of different types of technological innovation is to start considering uncertainty. This seems plausible since technological development and innovation almost always occur under conditions of uncertainty. We rely on the distinction between, on the one hand, uncertainty that can be quantified (e.g. probabilistic risk) and, on the other, deep forms of uncertainty that may resist the possibility of being quantified (e.g. severe or fundamental uncertainties). On the basis of these different ingredients of uncertainty in technological innovation, we propose a new taxonomy that reveals the technological nature of innovation. Unlike previous taxonomies employed to handle different types of technological innovations, our taxonomy does not consider the economic value of innovation alone; it is much more oriented towards societal preferences and forms of technological uncertainty. Finally, we investigate the coherence of our proposal with the dual nature of technological artefacts, showing that innovation can be grounded on structural and functional factors and not just on economic ones.

The potential development of self-driving cars (also known as autonomous vehicles or AVs – particularly Level 5 AVs) has called the attention of different interested parties. Yet, there are still only a few relevant international regulations on them, no emergency patterns accepted by communities and Original Equipment Manufacturers (OEMs), and no publicly accepted solutions to some of their pending ethical problems. Thus, this paper aims to provide some possible answers to these moral and practical dilemmas. In particular, we focus on what AVs should do in no-win scenarios and on who should be held responsible for these types of decisions. A naturalistic perspective on ethics informs our proposal, which, we argue, could represent a pragmatic and realistic solution to the regulation of AVs. We discuss the proposals already set out in the current literature regarding both policy-making strategies and theoretical accounts. In fact, we consider and reject descriptive approaches to the problem as well as the option of using either a strict deontological view or a solely utilitarian one to set AVs’ ethical choices. Instead, to provide concrete answers to AVs’ ethical problems, we examine three hierarchical levels of decision-making processes: country-wide regulations, OEM policies, and buyers’ moral attitudes. By appropriately distributing ethical decisions and considering their practical implications, we maintain that our proposal based on ethical naturalism recognizes the importance of all stakeholders and allows the most able of them to take actions (the OEMs and buyers) to reflect on the moral leeway and weight of their options.

This Special Issue titled "Severe Uncertainty in Science, Medicine and Technology" aims to shed new light on the understanding of severe uncertainty and its multifaceted implications. The main idea of the papers of this collection is that, despite possible sophisticated statistical judgments towards future risks in science, medicine, and technology, severe forms of uncertainty still exist.While ignorance is usually assumed to be a total absence of knowledge, uncertainty often refers to the incompleteness of knowledge or information. In its extreme form, this is called "severe uncertainty" but is also known as "fundamental," "radical," "deep," "great," or "genuine" uncertainty. A common characteristic of these...

Decision-making under uncertainty is sometimes investigated as a homogeneous problem, independently of the type of decision-maker and the level and nature of the decision itself. However, when the decision-maker is a public authority, there immediately arise problems additional to those that concern any other (private) decision-maker. This is not always clearly recognised in orthodox discussions on decisions under conditions of uncertainty. This article investigates the methodological, strategic and procedural challenges of taking public decisions in such conditions. It focuses mainly on decisions involving urban contexts, such as planning decisions regarding land use and building transformations, by trying to develop some pioneering research studies in this field.

This article presents and defends an integrated view of the placebo effect, termed “affective-meaning-making” model, which draws from theoretical reflection, clinical outcomes, and neurophysiological findings. We consider the theoretical limitations of those proposals associated with the “meaning view” on the placebo effect which leave the general aspects of meaning unspecified, fail to analyze fully the role of emotions and affect, and establish no clear connection between the theoretical, physiological, and psychological aspects of the effect. We point out that a promising way to overcome these limitations is given by grounding the placebo effect on Peirce’s theory of meaning, in which the role of the meaning constitution and change is placed in logical and objective structures. We also show the connection between our theoretical proposal and the appraisal theory and integrate it with emotion regulation.