We describe the rationale for an application ontology covering the domain of human body fluids that is designed to facilitate representation, reuse, sharing and integration of diagnostic, physiological, and biochemical data, We briefly review the Blood Ontology (BLO), Saliva Ontology (SALO) and Kidney and Urinary Pathway Ontology (KUPO) initiatives. We discuss the methods employed in each, and address the project of using them as starting point for a unified body fluids ontology resource. We conclude with (...) a description of how the body fluids ontology initiative may provide support to basic and translational science. (shrink)

In this paper, we will report annotation experiments which show the advantage of applying a formal ontological methodology for constructing a schema for semantic annotation to mark up terms in the...

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated (...) to the salivaomics domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

There is a need recognized by the National Institute of Dental & Craniofacial Research and the National Cancer Institute to advance basic, translational and clinical saliva research. The goal of the Salivaomics Knowledge Base (SKB) is to create a data management system and web resource constructed to support human salivaomics research. To maximize the utility of the SKB for retrieval, integration and analysis of data, we have developed the Saliva Ontology and SDxMart. This article reviews the informatics advances in (...) saliva diagnostics made possible by the Saliva Ontology and SDxMart. (shrink)

Despite the importance of human blood to clinical practice and research, hematology and blood transfusion data remain scattered throughout a range of disparate sources. This lack of systematization concerning the use and definition of terms poses problems for physicians and biomedical professionals. We are introducing here the Blood Ontology, an ongoing initiative designed to serve as a controlled vocabulary for use in organizing information about blood. The paper describes the scope of the Blood Ontology, its stage of development (...) and some of its anticipated uses. (shrink)

There are many connections among artificial intelligence, learning, reasoning and ontologies. The Ontology Summit 2017 explored, identified and articulated the relationships among these areas. As p...

There are many connections among artificial intelligence, learning, reasoning and ontologies. The Ontology Summit 2017 explored, identified and articulated the relationships among these areas. As p...

Over the past decade, variants of Gödel's ontological arguments have been critically examined using modern symbolic AI technology. Computers have unearthed new insights about them and even contributed to the exploration of new, simplified variants of the argument, which now need to be further investigated by theologians and philosophers. In this article, I provide a brief, informal overview of these contributions and engage in a discussion of the possible future role of AI technology for the rigorous assessment of arguments in (...) theology and philosophy. (shrink)

In this paper, we examine the meta-ontology of AI systems with human-level intelligence, with us denoting such AI systems as AI E. Meta-ontology in philosophy is a discourse centered on ontology, ontological commitment, and the truth condition of ontological theories. We therefore discuss how meta-ontology is conceptualized for AI E systems. We posit that the meta-ontology of AI E systems is not concerned with computational representations of reality in the form of structures, data constructs, or (...) computational concepts, while the ontological commitment of AI E systems is directed toward what exists in the outside world. Furthermore, the truth condition of the ontology (which is meta-ontological assumption) of AI E systems does not require consistency with closed conceptual schema or ontological theories but rather with reality, or in other words, “what is the world” (Smith, 2019, p.57). In addition, the truth condition of AI E systems is verified through operational success rather than by coherence with theories. This work builds on ontological postulates about AI systems that were formulated by Brian Cantwell Smith (2019). (shrink)

Volume 52, Issue 6, June - July 2020, Page 593-601.

AI’s new ability to create artworks is seen as a major challenge to today’s understanding of art. There is a strong tension between people who predict that AI will replace artists and critics who claim that AI art will never be art. Furthermore, recent studies have documented a negative bias towards AI art. This paper provides a philosophical explanation for this negative bias, based on our shared understanding of the ontological differences between objects. We argue that our perception of art (...) depends on our understanding of the context of its creation: human-made art is experienced as an interplay between the artist and nature. In AI-generated art, this interplay is either absent or minimised. We conclude that the displacement of the “human factor” in art will not lead to an evolution of art, but to the end of art. (shrink)

This paper offers an Aristotelian-Thomistic response to the question whether AI is capable of developing virtue. On the one hand, it could be argued that this is possible on the assumption of the minimalist (thin) definition of virtue as a stable (permanent) and reliable disposition toward an actualization of a given power in the agent (in various circumstances), which effects that agent’s growth in perfection. On the other hand, a closer inquiry into Aquinas’s understanding of both moral and intellectual virtues, (...) and a more detailed analysis of the ontological status of AI, show that it is highly unlikely to envision the design of specifically human-like reason-based and/or behavioral-based (“strong”) AI that would possess properly human virtues. Still, virtuous “weak AI” might be possible, although a question ought to be asked whether we should classify artifacts’ virtues using categories developed in reference to specifically human dispositions and actions. (shrink)

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...) intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy. (shrink)

This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify (...) two emerging techniques for enabling users’ document queries to better express the theories of relevance and connect them to documents: social network analysis and a hypothesis ontology. (shrink)

Artificial intelligence is the new technological buzzword. Everything from camera apps on your mobile phone to medical diagnosis algorithms to expert systems are now claiming to be ‘AI’, and many more facets of our lives are being colonized by the application of AI/ML systems (henceforth, ‘AI’). But what does this entail to designers, users and to society at large? Most of the philosophical discourse in this context has focused on the analysis and clarification of the epistemological claims of intelligence within (...) AI and on the moral implications of AI. Philosophical critiques of the plausibility of artificial intelligence do not have much to say about the real-world repercussions of introducing AI systems into every possible domain in the name of automation and efficiency; similarly, most of the moral misgivings about AI have to do with conceiving them as autonomous agents beyond the control of human actors. These discussions have clarified the debate surrounding AI to a great extent; however, certain crucial questions remain outside the ambit of these debates. Arguments in support of AI often take advantage of this void by emphasizing that AI systems are no different than previously existing ‘unintelligent’ technologies, thereby implying that the economic, existential, and ethical threats posed by these systems are either similar in kind as those posed by any other technology or grossly misplaced and exaggerated. In this chapter, we shall think through this assumption by investigating into the ontology of AI systems vis-à-vis ordinary (non-AI) technical artefacts to see wherein lies the distinction between the two. I shall examine how contemporary ontologies of technical artefacts (e.g., intentionalist and non-intentionalist theories of function) apply to AI. Hence, clarifying the ontology of AI is crucial to understand their normative and moral significance and the implications therefrom. (shrink)

A novel situation has developed in which one can discuss physics and ontology with an Artificial Intelligence. In this paper, I present my initial experience with such and discuss a typical session for analysis. After analyzing the session, I attempt to interpret the significance of AI for physics and suggest possible consequences of this situation.

Ontology development methodologies emphasise knowledge gathering from domain experts and documentary resources, and knowledge representation using an ontology language such as OWL or FOL. However, working ontologists are often surprised by how challenging and slow it can be to develop ontologies. Here, with a particular emphasis on the sorts of ontologies that are content-heavy and intended to be shared across a community of users (reference ontologies), we propose that a significant and heretofore under-emphasised contributor of challenges during (...) class='Hi'>ontology development is the need to create, or bring about, consensus in the face of disagreement. For this reason reference ontology development cannot be automated, at least within the limitations of existing AI approaches. Further, for the same reason ontologists are required to have specific social-negotiating skills which are currently lacking in most technical curricula. (shrink)

Legal codes, such as the Uniform Commercial Code (UCC) examined in this article, are good points of entry for AI and ontology work because of their more straightforward adaptability to relationship linking and rules-based encoding. However, approaches relying on encoding solely on formal code structure are incomplete, missing the rich experience of practitioner expertise that identifies key relationships and decision criteria often supplied by experienced practitioners and process experts from various disciplines (e.g., sociology, political economics, logistics, operations research). This (...) research focuses on the UCC because it transcends the limitations of a formal code, functioning essentially as a composite. AI work can benefit from real-world codes like the UCC, which are essentially formal codes enlightened from a more realistic experience-base from centuries of development in international commercial transactions settings. This paper then describes our initial work in converting an expert system on the U.S. law governing the sale of goods from Article II of the Uniform Commercial Code (UCC), into a knowledge-based system using the Web Ontology Language OWL. (shrink)

Over the last three decades, the rise of embodied cognition (EC) articulated in various schools (or versions) of embodied, embedded, extended and enacted cognition (Gallagher’s 4E) has offered AI a way out of traditional computationalism—an approach (or an understanding) loosely referred to as embodied AI. This view has split into various branches ranging from a weak form on the brink of functionalism (loosely represented by Clarks’ parity principle) to a strong form (often corresponding to autopoietic-friendly enactivism) suggesting that body−world interactions (...) constitute cognition. From an ontological perspective, however, constitution is a problematic notion with no obvious empirical or technical advantages. This paper discusses the ontological issues of these two approaches in regard to embodied AI and its ontological commitments: circularity, epiphenomenalism, mentalism, and disguised dualism. The paper also outlines an even more radical approach that may offer some ontological advantages. The new approach, called the mind-object identity, is then briefly compared with sensorimotor direct realism and with the embodied identity theory. (shrink)

Abstract:AI technologies mine past data to anticipate future events, and yet our world of environmental and political crisis ushers in unprecedented conditions. Mixing examples of operational environments (AI in the oil and gas industry) with insights from media, cultural, and environmental studies, this article explores this grappling with uncertainty. To manage uncertainty, companies strive to internalize the complexity and contingency of the real world, collecting more data, designing more accurate sensors, and developing more exhaustive models. And yet prediction is a (...) fraught exercise that struggles with correlation versus causation, the epistemological outside (the unknown), and the ontological outside (the open-endedness of the future). In addition, technology’s role in accelerating and intensifying the destructive logics of capital contributes to more volatile planetary conditions, undermining the stability and continuity that prediction requires. The article thus argues that, at a fundamental level, a highly fluid future will increasingly frustrate any meaningful degree of prediction. Keywords: prediction, knowledge, AI, machine learning, uncertainty, climate change. (shrink)

This paper interrogates the growing pervasiveness of affect recognition tools as an emerging layer human-centric automated management in the global workplace. While vendors tout the neoliberal incentives of emotion-recognition technology as a pre-eminent tool of workplace wellness, we argue that emotional AI recalibrates the horizons of capital not by expanding outward into the consumer realm (like surveillance capitalism). Rather, as a new genus of digital Taylorism, it turns inward, passing through the corporeal exterior to extract greater surplus value and managerial (...) control from the affective states of workers. Thus, empathic surveillance signals a profound shift in the ontology of human labor relations. In the emotionally quantified workplace, employees are no longer simply seen as physical capital, but conduits of actuarial and statistical intelligence gleaned from their most intimate subjective states. As a result, affect-driven automated management means that priority is often given to actuarial rather than human-centered managerial decisions. (shrink)

The paper identifies some of the problems with legal systems and outlines the potential of AI technology for overcoming them. For expository purposes, this outline is based on a simplified epistemology of the primary functions of law. Social and philosophical impediments from the side of the legal community to taking advantage of the potential of this technology are discussed and strategic recommendations are given.

Implicit stochastic models, including both ‘deep neural networks’ (dNNs) and the more recent unsupervised foundational models, cannot be explained. That is, it cannot be determined how they work, because the interactions of the millions or billions of terms that are contained in their equations cannot be captured in the form of a causal model. Because users of stochastic AI systems would like to understand how they operate in order to be able to use them safely and reliably, there has emerged (...) a new field called ‘explainable AI’ (XAI). When we examine the XAI literature, however, it becomes apparent that its protagonists have redefined the term ‘explanation’ to mean something else, namely: ‘interpretation’. Interpretations are indeed sometimes possible, but we show that they give at best only a subjective understanding of how a model works. We propose an alternative to XAI, namely certified AI (CAI), and describe how an AI can be specified, realized, and tested in order to become certified. The resulting approach combines ontologies and formal logic with statistical learning to obtain reliable AI systems which can be safely used in technical applications. (shrink)

In this paper I discuss the commentaries and the criticism that my friends and colleagues have made to the theory of social objects that I put forward in my book Documentalità. Perché è necessario lasciar tracce. In particular, I have articulated my responses along the following main lines: realism; truth (and falsity); ontology vs. epistemology and facts vs. interpretations; textualism and writing; politics; intentionality; consciousness.

The emergence of AI is posing serious challenges to standard conceptions of moral status. New non-biological entities are able to act and make decisions rationally. The question arises, in this regard, as to whether AI systems possess or can possess the necessary properties to be morally considerable. In this chapter, we have undertaken a systematic analysis of the various debates that are taking place about the moral status of AI. First, we have discussed the possibility that AI systems, by virtue (...) of its new agential capabilities, can be understood as a moral agent. Discussions between those defending mentalist and anti-mentalist positions have revealed many nuances and particularly relevant theoretical aspects. Second, given that an AI system can hardly be an entity qualified to be responsible, we have delved into the responsibility gap and the different ways of understanding and addressing it. Third, we have provided an overview of the current and potential patientist capabilities that AI systems possess. This has led us to analyze the possibilities of AI possessing moral patiency. In addition, we have addressed the question of the moral and legal rights of AI. Finally, we have introduced the two most relevant authors of the relational turn on the moral status of AI, Mark Coeckelbergh and David Gunkel, who have been led to defend a relational approach to moral life as a result of the problems associated with the ontological understanding of moral status. (shrink)

We begin at the beginning, with an outline of Aristotle’s views on ontology and with a discussion of the influence of these views on Linnaeus. We move from there to consider the data standardization initiatives launched in the 19th century, and then turn to investigate how the idea of computational ontologies developed in the AI and knowledge representation communities in the closing decades of the 20th century. We show how aspects of this idea, particularly those relating to the use (...) of the term 'concept' in ontology development, influenced SNOMED CT and other medical terminologies. Against this background we then show how the Foundational Model of Anatomy, the Gene Ontology, Basic Formal Ontology and other OBO Foundry ontologies came into existence and discuss their role in the development of contemporary biomedical informatics. (shrink)

The purpose of the article: to clarify the role of the body used by the AGI as a verifier of its cognitive activity.It is shown that the concept of «embodied mind» potentially removes the basic contradiction of general AI: cognitive activity cannot be fully reflected, and the technogenic nature of AI requires the maximization of self-reflection. The body for general AI can be a tool for overcoming the limitations of Gödel’s incompleteness theorem.A contradiction is described: between the need for the (...) widest possible use of publicly available “digital twins”, collective knowledge of the world and the need for general AI to have an ontological verifier as the basis for its own subjectivity. A form of manifestation of this contradiction can be considered as restriction of access to the results of research conducted by AI: secrecy reduces the capabilities of general AI as a subject, but increases its independence as an individual;It has been proven that the body of general AI cannot be a sufficient ontological verifier; cooperation with other subjects and the use of sensors external to its body are necessary;It has been shown that while maintaining the rate of development of communications, the body of general AI, as an individual, can be considered as part of the technosphere, which it controls better than all other AI. The independence of general AI, as an individual, is ensured by using the body as a verifier of social interactions. And the body of general AI, as a subject, is that part of the technosphere which it sets goals and correlates with reality better than other subjects;The dynamic nature of the corporeality of general AI has been established: in the context of the rapid development of artificial intelligence capabilities, it is necessary to constantly improve your body and deepen its reflection for use as a verifier in both senses;It has been established that the body of general AI, upon achieving a balance of ontological and social verification, can be an instrument for removing the contradiction between AI, as an individual, and AI, as a subject.However, the problem of the relationship between the concepts of “verifier of social interactions” and “social body” remains unanalyzed. (shrink)

Scenarios involving the introduction of artificially intelligent (AI) assistive technologies in health care practices raise several ethical issues. In this paper, I discuss four objections to introducing AI assistive technologies in health care practices as replacements of human care. I analyse them as demands for felt care, good care, private care, and real care. I argue that although these objections cannot stand as good reasons for a general and a priori rejection of AI assistive technologies as such or as replacements (...) of human care, they demand us to clarify what is at stake, to develop more comprehensive criteria for good care, and to rethink existing practices of care. In response to these challenges, I propose a (modified) capabilities approach to care and emphasize the inherent social dimension of care. I also discuss the demand for real care by introducing the ‘Care Experience Machine’ thought experiment. I conclude that if we set the standards of care too high when evaluating the introduction of AI assistive technologies in health care, we have to reject many of our existing, low-tech health care practices. (shrink)

Scholarship on embedding values in AI is growing. In what follows, we distinguish two concepts of AI and argue that neither is amenable to values being ‘embedded’. If we think of AI as computational artifacts, then values and AI cannot be added together because they are ontologically distinct. If we think of AI as sociotechnical systems, then components of values and AI are in the same ontologic category—they are both social. However, even here thinking about the relationship as one of (...) ‘embedding’ is a mischaracterization. The relationship between values and AI is best understood as a dimension of the relationship between technology and society, a relationship that can be theorized in multiple ways. The literature in this area is consistent in showing that technology and society are co-productive. Within the co-production framework, the relationship between values and AI is shown to be generative of new meaning. This stands in stark contrast to the framework of ‘embedding’ values which frames values as fixed things that can be inserted into technological artifacts. (shrink)

The attempt by the digital forces to ‘naturalize’ the digital and thus to make it one with our ontology raises a whole host of issues about how to identify the Self. The multi-pronged process of naturalization are driven by a particular dynamic: the ‘more’ of data. Data is not a static pile of information, but only works within strategies of accumulation. Businesses and academe have bought into this strategy – addicted to its potential for control – in ways that (...) make it impossible to see ‘an outside’. This ‘more’ is, however, hardly foolproof, and is in fact designed around a wide range of fallibilities – some visible, but most not - that are also now part of the new natural. The resultant dialectic is unstable and as it operates to re-engineer our sense of Self it faces its own destiny. (shrink)

Ontologies, as the term is used in informatics, are structured vocabularies comprised of human- and computer-interpretable terms and relations that represent entities and relationships. Within informatics fields, ontologies play an important role in knowledge and data standardization, representation, integra- tion, sharing and analysis. They have also become a foundation of artificial intelligence (AI) research. In what follows, we outline the Coronavirus Infectious Disease Ontology (CIDO), which covers multiple areas in the domain of coronavirus diseases, including etiology, transmission, epidemiology, pathogenesis, (...) diagnosis, prevention, and treatment. We emphasize CIDO development relevant to COVID-19. (shrink)

The article endeavours to understand and explain the position of AI humanoids in society. It further makes a unique attempt to describe humanoid robots as “artificial persons,” and while doing so, it sheds light on intriguing, less-debated topics like relationships between humans and artificially intelligent humanoids (AI) and the identity of AI humanoids. The goal of this manuscript is to present the argument that suggests that artificially intelligent humanoid robots are a remarkable creation of human ingenuity and are distinct from (...) typical machines, as they exhibit qualities that extend beyond mere mechanics and aim to reach a degree of complexity akin to humans. These humanoids can form strong connections with humans and possess the ability to communicate, learn, generate information, and assist humans in a multitude of tasks. Therefore, it seems logical to attempt to describe these humanoids as “artificial persons” and try giving them a unique identification or digital signature that may be related in some manner to their owner’s biometric identity. The manuscript employs text analysis to support its arguments and, in the process, delves into the solutions to the aforementioned issues by exploring the concepts proposed by renowned theorists, classical philosophers, and psychologists. (shrink)

With the increasing amount of software devoted to industrial automation and process control, it is becoming more important than ever for systems to be able to explain their behavior. In some domain...

This paper explores the human experience of game-playing and its implications for artificial intelligence. The author uses phenomenology to examine game-playing from a human-centered perspective and applies it to language games played by artificial intelligences and humans. The paper argues that AI cannot truly play games because it lacks the intentionality, embodied experience, and social interaction that are fundamental to human game-playing. Furthermore, current AI lacks the ability to converse, which is argued to be equivalent to Wittgenstein’s view of engaging (...) in “language games.” The author presents a formal ontology of game playing, agents, and their intentional states, and contrasts human agents playing games with artificial agents “playing games.” The paper concludes that while artificial intelligence can simulate some aspects of human language games, it lacks the essential capacities for game playing, such as cognition, creativity, will, intentionality, understanding, and personhood. (shrink)

Reasoning about causation in fact is an essential element of attributing legal responsibility. Therefore, the automation of the attribution of legal responsibility requires a modelling effort aimed at the following: a thorough understanding of the relation between the legal concepts of responsibility and of causation in fact; a thorough understanding of the relation between causation in fact and the common sense concept of causation; and, finally, the specification of an ontology of the concepts that are minimally required for (automatic) (...) common sense reasoning about causation. This article offers a worked-out example of the indicated analysis. Such example consists of: a definition of the legal concept of responsibility (in terms of liability and accountability); a definition of the legal concept of causation in fact (in terms of the initiation of physical processes by an agent and of the provision of reasons and/or opportunities to other agents); CausatiOnt, an AI-like ontology of the common sense (causal) concepts that are minimally needed for reasoning about the legal concept of causation in fact (in particular, the concepts of category, dimension, object, agent, process, event and act). (shrink)

The phenomenon of smiling is the central theme of this text. Despite the risks and difficulties inherent to a topic little covered by Heidegger and also by secondary literature, we start from an assumption that presents itself as an unavoidable fact: the smile is one of the human being's faculties. Not only that: as Aristotle already said, he is the only being who laughs. In other words: somehow the smile makes him unique, distinguishing him from animals. Taking this singularity into (...) consideration, we intend to reflect on whether smiles, in some way, are in line with the ontological condition of the human being. (shrink)

Artificial intelligence increasingly executes tasks that previously only humans could do, such as drive a car, fight in war, or perform a medical operation. However, as the very best AI systems tend to be the least controllable and the least transparent, some scholars argued that humans can no longer be morally responsible for some of the AI-caused outcomes, which would then result in a responsibility gap. In this paper, I assume, for the sake of argument, that at least some of (...) the most sophisticated AI systems do indeed create responsibility gaps, and I ask whether we can bridge these gaps at will, viz. whether certain people could take responsibility for AI-caused harm simply by performing a certain speech act, just as people can give permission for something simply by performing the act of consent. So understood, taking responsibility would be a genuine normative power. I first discuss and reject the view of Champagne and Tonkens, who advocate a view of taking liability. According to this view, a military commander can and must, ahead of time, accept liability to blame and punishment for any harm caused by autonomous weapon systems under her command. I then defend my own proposal of taking answerability, viz. the view that people can makes themselves morally answerable for the harm caused by AI systems, not only ahead of time but also when harm has already been caused. (shrink)

The year 2012 has witnessed worldwide celebrations of Alan Turing’s 100th birthday. A great number of conferences and workshops were organized by logicians, computer scientists and researchers in AI, showing the continued flourishing of computer science, and the fruitful interfaces between logic and computer science. Logic is no longer just the concept that Frege had about one hundred years ago, let alone that of Aristotle twenty centuries before. One of the prominent features of contemporary logic is its interdisciplinary character, connecting (...) across mathematics, philosophy, modern computer science, and even the cognitive and social sciences. -/- China is no exception. This special issue explores interfaces of logic with AI and other disciplines, and it attempts to give a bird’s eye view of the developing landscape of logic and AI in China. Our contributors come from computer science, AI, philosophy and linguistics, united by their interest in using formal logical tools and notions to deal with substantial problems in their fields. We are aware that AI is a vast area, and that the limitations of space in a volume like this may provide a somewhat incomplete or biased view. Still we hope that the six papers selected here illustrate the concerns and curiosity driving Chinese researchers, on topics shared with our colleagues around the world. -/- One of the active research areas in AI is planning under uncertainty, with incomplete knowledge, where a well-established logical framework is the action language of Son and Baral (Son and Baral 2001). This language can express the notions of sensing actions and making conditional plans. However, its current semantics is not yet satisfactory, since according to the semantics an agent would not have knowledge of what happens after a plan is executed, which can be a serious problem in a safety-critical environment. In their paper Proof Systems for Planning under Cautious Semantics (Shen and Zhao 2013), Yuping Shen and Xishun Zhao address this problem and propose a cautious and weakly cautious semantics for the action language. This new semantics enables the agent to know exactly what happens after executing an action. Hoare style proof systems are then proposed and proved to be sound and complete. What is more, the proposed new system reduces the reasoning complexity of the action language. -/- In Preferential Semantics for Plausible Subsumption in Possibility Theory (Qi and Zhang 2013), Guilin Qi and Zhizheng Zhang investigate another important issue in AI: how to handle exceptions in a knowledge-based system. This has become a hot topic in recent years, especially in ontological design, where description logics are widely used. The question then arises whether we can get a nonmonotonic extension of description logic. To answer this, the authors first propose three preferential semantics for plausible subsumption in the framework of possibility theory (Dubois and Prade 1985) and study their properties and their relationships. Then they introduce preferential subsumptions to a description logic-based knowledge system, and define an entailment relation of plausible subsumption. Moreover, they study reductions of the new semantics to standard semantics for an expressive description logic. -/- Counterfactual reasoning has been explored extensively by philosophers, cf. Lewis (1973), Stalnaker (1968). But in the last two decades, causal models have been proposed for modeling counterfactual reasoning by researchers in AI. In A Lewisian Logic of Causal Counterfactuals (Zhang 2013), Jiji Zhang studies classes of causal models and provides formal characterizations. In particular, a new logic system is proposed and proved complete for the class of ‘Lewisian models’ satisfying all the principles of David Lewis’s logic of counterfactuals. Moreover, Zhang compares his new framework with those of Lewis and Stalnaker, and also establishes formal connections with more recent approaches by Galles and Pearl, and by Halpern. -/- Consistency is a fundamental notion in logic, but it also plays a key role in many techniques for solving constraint satisfaction problems (Mackworth 1977). In Variable-Centered Consistency in Model RB (Li et al. 2013), Liang Li, Tian Liu and Ke Xu start with a discussion of i-consistency in ‘Model RB’ and ‘Model RD’, two models of random constraint satisfaction problems with growing domain size, then compare it with the notion of t-consistency and strong t-consistency in well-known earlier work by Freuder. After that, the authors define a new kind of variable-centered consistency, and show an upper bound on a suitably chosen parameter in Model RB and Model RD, such that up to this bound, all typical instances of Model RB are variable-centered consistent. -/- Language understanding is a cognitive process that human beings carry out every day. Metaphor is a pervasive phenomenon in natural languages, and its cognitive role calls for interpretation. There is a growing body of literature on cognitive mechanisms underlying metaphor. The paper An Ontology-Based Approach to Metaphor Cognitive Computation (Huang et al. 2013) by Xiaoxi Huang, Huaxin Huang, Beishui Liao, and Cihua Xu proposes a theoretical framework for metaphor understanding based on recent results from cognitive science (Shutova 2010). They then introduce ontology as a knowledge representation method, and present a new ontological model to formalize metaphor-based knowledge. An algorithmic description is provided using a quantitative measure of integrated degree, and its effectiveness is demonstrated. -/- Puzzles and their solution mechanisms are a recurrent theme in many fields. Dynamic epistemic logic (van Ditmarsch et al. 2007); (van Benthem 2011) is a recent framework that can handle information dynamics in knowledge puzzles. In Reasoning About Agent Types and the Hardest Logic Puzzle Ever (Liu and Wang 2013), Fenrong Liu and Yanjing Wang propose a new dynamic-epistemic logic that can specify different types of agents and analyse agents’ knowledge, communication and reasoning. To demonstrate its power, Smullyan’s well-known Knights and Knaves puzzles are formalized, as well as the Hardest Logic Puzzle Ever (HLPE) of Boolos (Boolos 1996). Moreover, a spectrum of new puzzles is found involving knowledge-based agent types, and their solution complexity is shown to be harder than that for existing logic puzzles. It is also shown that a version of HLPE in which the agents do not know the others’ types does not have a solution at all. -/- Finally, we would like to thank the editor of Minds and Machines, Gregory Wheeler, for his efficient assistance and kind support. We also thank Ties Nijssen at Springer for his encouragement to start this project at the first place. And of course, our appreciation goes to our authors, as well as all the colleagues who have helped us review the papers in this collection. (shrink)

Legal Information Retrieval (IR) research has stressed the fact that legal knowledge systems should be sufficiently capable to interpret and handle the semantics of a database. Modeling (expert-) knowledge by using ontologies enhances the ability to extract and exploit information from documents. This contribution presents theories, ideas and notions regarding the development of dynamic electronic commentaries based on a comprehensive legal ontology.

The ethics of artificial intelligence (AI) is a research topic with both theoretical and practical significance. However, the ethical and moral aspects of conceptual, ontological, semantic, and knowledge modeling, more specifically, and which are sometimes found in AI applications, is not being given sufficient attention. I argue that it should. Whether considering using or developing these meaning-focused models, there are ethical aspects. This paper offers a preliminary outline about this potentially new research field, discussing: some questions and areas of concern, (...) their significance; scenarios of ethical import, and initial ethical guidelines. A focus area, potential case study, and lens through which to see the ethical significance at hand, is ontologies, with emphasis on the most abstract ontologies (so-called generic, foundational, top-level or upper ontologies). This would aim to show the ethical import of highly abstract (often philosophical) ontological choices, assumptions, and their effects on data, developers, users, and society. In particular, I stress the need for critical examination of such models, their content, their intended meanings, assumptions, ideologies, as well as the development process. Both the ethics of, and ethics in, these activities are, therefore, of interest. Topics in the ethics of AI that are relevant include: transparency, bias, data-sharing, privacy, control over data, etc. This paper thereby contributes to the ethics of AI, as well as the ethics of information, of knowledge managment and relevant topics by developing and discussing the ethics of ontology (and similar systems). Future development will refine and add to the ideas expressed in this paper. (shrink)

Movement is crucial to Merleau-Ponty’s effort to comprehend sense, meaning as generated within being. This requires a new concept of movement, not as a dislocation within an already determinate space- or time- frame, but as a deeper, more fundamental change that first engenders space and time as determinate contexts in which movement can follow a sensible course. This poses a novel challenge: conceptualizing determinate space and time as contingently arising from a deeper sort of change, which I call templacement. I (...) address this challenge by turning to the Mexica/Aztecs because the most basic term of their ontology is motion-change, and it is obvious to them that motion-change does not occur in an abstract space-time container. Instead, time-place is woven out of ‘prior’ motion-change. This study leads to a deeper lesson for phenomenology, regarding ‘obvious’ presuppositions about what time and philosophy obviously are – and how these presuppositions go hand in hand.La question du mouvement est cruciale dans l’effort de Merleau-Ponty pour comprendre le sens, la signification telle qu’elle se produit dans l’être. Cela requiert un nouveau concept du mouvement, qui n’est pas compris comme un déplacement dans un cadre spatial – ou temporel – prédéterminé, mais plutôt comme un changement plus profond et fondamental, qui, avant tout, produit l’espace et le temps comme des contextes déterminés dans lesquels le mouvement peut suivre un cours sensible. Cela pose un nouveau défi : conceptualiser un espace et un temps déterminés qui résulteraient, de manière contingente, d’un type de changement plus profond, que j’appellerai templacement. Je lance ce défi en me tournant vers les Mexicas/Aztèques parce que le terme le plus élémentaire de leur ontologie est le mouvement-changement, et il est naturel pour eux que le mouvement-changement n’ait pas lieu dans un conteneur spatio-temporel abstrait. Au contraire, le temps-lieu est tissé sans mouvement-changement « préalable ». Cette recherche conduit à un enseignement encore plus profond pour la phénoménologie, concernant au sujet des présupposition “évidentes” sur le temps et sur la philosophie, et elle nous montre aussi comment ces présuppositions sont étroitement liées.Il movimento è un elemento cruciale nello sforzo di Merleau-Ponty di comprendere il senso, il significato in quanto generato all’interno dell’essere. Questo sforzo richiede un nuovo concetto di movimento, da intendere non come movimento-spostamento nell’ambito di una cornice spazio-temporale predefinita, ma come più profondo e più fondamentale mutamento che inaugura lo spazio e il tempo, in quanto contesti nei quali il movimento stesso può esprimere una traiettoria dotata di senso. Si pone quindi una nuova sfida: quella di concettualizzare lo spazio e il tempo come generati da una forma più radicale di mutamento, che propongo di definire templacement. Per affrontare questa sfida, mi rivolgo ai Mexica/Aztechi poiché il termine più basilare della loro ontologia è quello del moto-mutamento, e poiché essi ritengono ovvio che questo moto-mutamento non si verifichi all’interno di un contenitore spazio-temporale astratto: al contrario, lo spazio-tempo è per loro un intreccio che risulta da un moto-mutamento “precedente”. Questo studio permette di ricavare una lezione più profonda per la fenomenologia, che concerne le sue “ovvie” presupposizioni riguardo a che cosa siano “ovviamente” il tempo e la filosofia – e a come queste presupposizioni siano strettamente connesse. (shrink)

In this paper we analyse the conditions for attributing to AI autonomous systems the ontological status of “artificial moral agents”, in the context of the “distributed responsibility” between humans and machines in Machine Ethics (ME). In order to address the fundamental issue in ME of the unavoidable “opacity” of their decisions with ethical/legal relevance, we start from the neuroethical evidence in cognitive science. In humans, the “transparency” and then the “ethical accountability” of their actions as responsible moral agents is not (...) in contradiction with the unavoidable “opacity” (unawareness) of the brain process by which they perform their moral judgements on the right action to execute. In fact, the moral accountability of our actions depends on what is immediately before and after our “moral judgements” on the right action to execute (formally, deontic first order logic (FOL) decisions). I.e., our moral accountability depends on the “ethical constraints” we imposed to our judgement before performing it in an opaque way. Anyway, our moral accountability depends overall on the “ethical assessment” or explicit “moral reasoning” after and over the moral judgement before executing our actions (deontic higher order logic (HOL) assessment). In this way, in the light of the AI “imitation game”, the consistent attribution of the status of ethically accountable artificial moral agents to autonomous AI systems depends on two similar conditions. Firstly, it depends on the presence of “ethical constraints” to be satisfied in their Machine Learning (ML) supervised optimization algorithm during its training phase, to give the system ethical skills (“competences”) in its decisions. Secondly – and definitely—, it depends on the presence in an AI autonomous system of a deontic HOL “ethical reasoner” to perform an automatic, and fully transparent assessment (metalogical deontic valuation) about the decisions taken by the ethically skilled ML algorithm about the right action to execute, before executing it. Finally, we show that the proper deontic FOL and HOL for this class of artificial moral agents is Kripke’s modal relational logic, in its algebraic topological formalization. This is naturally implemented in the dissipative QFT unsupervised deep learning of our brains, based on the “doubling of the degrees of freedom” (DDF), and then in the so-called “deep-belief” artificial neural networks for the statistical data pre-processing. This unsupervised learning procedure is also compliant with the usage of the “maximin fairness principle”, used as a balancing aggregation principle of the statistical variables in Sen’s formal theory of fairness. (shrink)

Deepfakes, as hyperrealistic digital fabrications, reveal gaps and uncertainties in existing ontological frameworks. Neither simply images nor realities, deepfakes occupy an ambiguous metaphysical position between concepts such as representation/simulation, human/machine, and real/artificial. Their emergent generation via AI and experiential traction as credible synthetic media underscores limitations in prevailing paradigms reliant on purified binaries and anthropocentric assumptions. Rather than anomalies, deepfakes epitomize the imperative for new ontological cartographies and conceptual vocabularies attuned to increasingly unbounded algorithmic creation. The paper surveys debates about (...) digital objects and theories of representation to contextualize this argument. Deepfakes’ ontological novelty stems from their ambiguous origins, autonomous proliferation, and infiltration of perceivable situations with credible fictions. This destabilizes ontological foundations such as creative intentionality and indexical truth. I conclude deepfakes signal the insufficiency of existing ontology for navigating realities transformed by generative technologies. New paradigms are needed that grapple with their radical metaphysical challenges. (shrink)

The article seeks to highlight the relation between ontology and communication while considering the role of AI in society and environment. Bioinformationalism is the technical term that foregrounds this relationality. The study reveals instructive consequences for philosophy of technology in general and AI in particular. The first section introduces the bioinformational approach to AI, focusing on three critical features of the current AI debate: ontology of information, property-based vs. relational AI, and ontology vs. constitution of AI. When (...) applied to the themes of relationality and non-anthropocentric communications, bioinformational insights highlight an inclusive and meaningful groundwork for understanding AI by ‘relating’ it with society and the environment through an engagement with the ongoing critique of human supremacy. In the second section, we move from ‘relating’ AI to ‘rewilding’ AI by proposing taxonomical classification for certain technological entities. We situate our proposal in the broader personhood debate with the proposal of taxonomical ranking. In the last section, we show an instance of a relational approach steeped in substantialist ontology by introducing the fourth feature of the AI debate. A broad critique of Floridi’s philosophy of information introduces this fourth feature from the domain of philosophy and sociology to address various theoretical and ecological problems with current relational accounts. In doing so, we argue for ‘communication’ to be the replacement of ‘information’ as the moral unit. A bioinformational understanding of AI advocates taking ontological commitments seriously at all levels of informational and technological processes and products. (shrink)

Despite facing significant criticism for assigning human-like characteristics to artificial intelligence, phrases like “trustworthy AI” are still commonly used in official documents and ethical guidelines. It is essential to consider why institutions continue to use these phrases, even though they are controversial. This article critically evaluates various reasons for using these terms, including ontological, legal, communicative, and psychological arguments. All these justifications share the common feature of trying to justify the official use of terms like “trustworthy AI” by appealing to (...) the need to reflect pre-existing facts, be it the ontological status, ways of representing AI or legal categories. The article challenges the justifications for these linguistic practices observed in the field of AI ethics and AI science communication. In particular, it takes aim at two main arguments. The first is the notion that ethical discourse can move forward without the need for philosophical clarification, bypassing existing debates. The second justification argues that it’s acceptable to use anthropomorphic terms because they are consistent with the common concepts of AI held by non-experts—exaggerating this time the existing evidence and ignoring the possibility that folk beliefs about AI are not consistent and come closer to semi-propositional beliefs. The article sounds a strong warning against the use of human-centric language when discussing AI, both in terms of principle and the potential consequences. It argues that the use of such terminology risks shaping public opinion in ways that could have negative outcomes. (shrink)