The first volume to offer a thorough and systematic account of evidentiality and the expression of information source, Illustrated with extensive data from a range of typologically diverse languages, Introductory chapter offers practical advice for fieldworkers investigating evidentially, Interdisciplinary in nature with insights from typology, semantics, pragmatics, language description, anthropology, cognitive psychology, and psycholinguistics Book jacket.

Both Morain and Largent (2023) and Garland, Morain, and Sugarman (2023) recognize the ethical challenges inherent in clinician participation in embedded research. Focusing on the question of integr...

As the range of potential uses for Artificial Intelligence, in particular machine learning, has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of (...) AI ethics principles and the practical design of AI systems. In previous work, we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible or too strict. This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service.’. (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)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...) lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature. (shrink)

The goal of this article is to direct a jurisprudential analysis on the practical Fourth Amendment mechanism for controlling the American government authorities’ prospective privacy invasions under artificial intelligence (AI) surveillance. With AI surveillance, the American government authorities could commit massive privacy invasions against the Fourth Amendment. The fear of destructive AI privacy invasions seems eclectic. Disregarding this fear, this article maintains that the current U.S. constitutional institutions within the scope of the Fourth Amendment can still provide a practical mechanism (...) for controlling the governments’ AI privacy invasions. However, we can seldom find a substantial jurisprudential analysis on this issue. Under a pragmatic Fourth Amendment framework, this article fills out the void by analyzing certain prospects of this practical mechanism from a pragmatic perspective of analogical common law reasoning. Based on the analysis, legal reflects will be propounded. (shrink)

The objective of this study is to examine the effects of generative artificial intelligence (AI) tools, with a specific focus on ChatGPT, on the analytical proficiencies of policy analysts operating in Indonesia. Considering the increasing intricacies of contemporary governance and the emergence of "wicked problems," this study investigates the potential of AI to facilitate the development of inventive, data-centric public policies. Involving postgraduate students in a quasi-experimental design, this study investigated the efficacy of ChatGPT in assisting in the development of (...) policy solutions pertaining to air pollution in Jakarta. Specialized prompt engineering techniques are utilized in this study to enhance the utility of ChatGPT for policy analysis. The results suggest that, although generative AI tools such as ChatGPT provide systematic frameworks for tackling complex policy issues and may enhance analytical skills, they still require additional refinement to increase their empirical support and practical applicability in the Indonesian policy context. In accordance with incrementalism theory, the study concludes that incorporating AI into policy analysis can facilitate the gradual formulation of evidence-based policies, offering a pragmatic approach to the resolution of complex problems in the public sector. (shrink)

The introduction of results of AI and Law research in actual legal practice advances disturbingly slow. One of the problems is that most research can be classified as either theoretical or pragmatic, while combinations of these two are scarce. This interferes with the need for feedback as well as with the need of getting support, both financially and from actual legal practice. The conclusion of this paper is that an emphasis on research that generates operational and sophisticated systems is necessary (...) in order to provide a future for AI and Law. (shrink)

In this article, I review textual evidence demonstrating that James and Dewey incorporated Kant’s ideas, even while criticizing him. I specifically argue that the pragmatic evolution of the Kantian a priori carried out by James and Dewey is a transition from the mental to the bodily. I further argue that the parallels between pragmatists and Kant, along with the transition from the mental to bodily, relate to scientific contexts in which all developed their outlooks. Though historically grounded, my ultimate goal (...) is to show that pragmatism and by extension Kantianism mesh with and indeed contribute to cutting edge ideas in fields ranging from neuropathology to robotics and AI to cognitive science, whether in the form of Gibsonian theory or enactivism. (shrink)

The article responds to the recent evolution in AI judiciary, especially as presented in China. The authors compare the basic methodological background of Western and Eastern legal systems, concluding that the West is rather inclined to post-positivist methodology in law, which seems incompatible with the full use of AI in legal decision-making. In China, with its more pragmatic approach, the actual process of decision-making might be closer to a new form of technological legal positivism, distinct from Western trends in jurisprudence. (...) Finally, various forms of applicability of AI in law and judiciary are pondered upon, so as to use at least some positive aspects of AI in this sector—albeit with reservations regarding the decision-making proper. (shrink)

While there has been much discussion of whether AI systems could function as moral agents or acquire sentience, there has been relatively little discussion of whether AI systems could have free will. In this article, I sketch a framework for thinking about this question. I argue that, to determine whether an AI system has free will, we should not look for some mysterious property, expect its underlying algorithms to be indeterministic, or ask whether the system is unpredictable. Rather, we should (...) simply ask whether it is explanatorily indispensable to view the system as an intentional agent, with the capacity for choice between alternative possibilities and control over the resulting actions. If the answer is “yes”, then the system counts as having free will in a pragmatic and diagnostically useful sense. (shrink)

An epistemology mainly oriented at a philosophical discussion of natural sciences and technology is sketched out on the basis of the author’s “methodological scheme-interpretationism” combining a realistic and a perspectival pragmatic approach. - In the main part, 12 characteristic features of the New Technologies are presented and discussed as, e.g., operationalization, computerization, models and modularity, virtuality and artificiality, interdisciplinary interaction, comprehensive and complex systems, telematization and remote control, robotics and AI technology and automatization as well as “socio-eco-techno-systems”, technology-driven globalization and (...) the respective problems of individual and social responsibility. - Also, actual trends are listed and future tasks for the international ecological cooperation of states, UN and UNESCO bodies under urgent guidelines of humanitarian values and “practical/concrete humanity” are recommended. (shrink)

In this paper we, an epistemologist and a machine learning scientist, argue that we need to pursue a novel area of philosophical research in AI – the ethics of belief for AI. Here we take the ethics of belief to refer to a field at the intersection of epistemology and ethics concerned with possible moral, practical, and other non-truth-related dimensions of belief. In this paper we will primarily be concerned with the normative question within the ethics of belief regarding what (...) agents – both human and artificial – ought to believe, rather than with questions concerning whether beliefs meet certain evaluative standards such as being true, being justified, constituting knowledge, etc. We suggest four topics in extant work in the ethics of (human) belief that can be applied to an ethics of AI belief: doxastic wronging by AI (morally wronging someone in virtue of beliefs held about them); morally owed beliefs (beliefs that agents are morally obligated to hold); pragmatic and moral encroachment (cases where the practical or moral features of a belief is relevant to its epistemic status, and in our case specifically to whether an agent ought to hold the belief); and moral responsibility for AI beliefs. We also indicate two relatively nascent areas of philosophical research that haven’t yet been generally recognized as ethics of AI belief research, but that do fall within this field of research in virtue of investigating various moral and practical dimensions of belief: the epistemic and ethical decolonization of AI; and epistemic injustice in AI. (shrink)

In this essay, I challenge the egocentric tradition which privileges the standpoint of an isolated individual, and propose a speech-based dialogical approach as an alternative. Considering that the egocentric tradition can be deciphered in part by analyzing the distortions undergone by pronominal discourse in the language of classical philosophy, I reexamine the pragmatics of ordinary language featuring the pronoun I in an effort to recover a more relational understanding of persons. I develop such an analysis of the deep grammar (...) of pronominal discourse under the heading of ‘I–you connectedness’. I–you connectedness emphasizes the communicative structure of experience, in particular the phenomenological importance of the addressee, the inseparability of ‘I’ and ‘You’, and the nature of the alternation between them. I–you connectedness is the best thematized within living speech, which is invariably oriented towards an interlocutor, and animated by mutual address. Yet, I–you connectedness extends beyond living speech to other modalities of meaning, notably thinking and writing, where one notes a similar orientation to a living, imagined, or virtual addressee. I–you connectedness extends, therefore, beyond discursive experience, and captures the deep dialogic dimension of meaning in pre-linguistic and extra-linguistic life. I briefly consider its implications for theoretical and practical issues within robotics and AI. (shrink)

When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant that they can be ignored or one is able to recognise the circumstances when they will be such that the partial model applies. The transference of knowledge from the point of application to the point of learning utilises a combination of recognition and inference ­ a simple model of (...) the important features is learnt and later situations where inferences can be drawn from the model are recognised. Context is an abstraction of the collection of background features that are later recognised. Different heuristics for recognition and model formulation will be effective for different learning tasks. Each of these will lead to a different type of context. Given this, there are (at least) two ways of modelling context: one can either attempt to investigate the contexts that arise out of the heuristics that a particular agent actually applies (the `internal' approach); or (if this is feasible) one can attempt to model context using the external source of regularity that the heuristics exploit. There are also two basic methodologies for the investigation of context: a top-down (or `foundationalist') approach where one tries to lay down general, a priori principles and a bottom-up (or `scientific') approach where one can try and find what sorts of context arise by experiment and simulation. A simulation is exhibited which is designed to illustrate the practicality of the bottom-up approach in elucidating the sorts of internal context that arise in an artificial agent which is attempting to learn simple models of a complex environment. It ends with a plea for the cooperation of the AI and Machine Learning communities as both learning and inference is needed if context is to make complete sense. (shrink)

Do AI programs just make it quicker and easier for humans to do what they can do already, or can the range of do-able things be extended? This paper suggests that cognitively-oriented technology can make it possible for humans to construct and carry out mental operations, which were previously impossible. Probable constraints upon possible human mental operations are identified and the impact of cognitive technology upon them is evaluated. It is argued that information technology functions as a cognitive prosthetic enhancing (...) human intelligence and planning capabilities. Boundaries and constraints which Kant, Whorf, and many post-modernist theorists have seen as intrinsic to human cognition now cease to apply. (shrink)

This paper considers the potentially important role played by non-verbal communication in constraining pragmatic processing. Attention is paid to claims about the role of emotion in memory encoding and recall, its role in the formulation of plans and goals, and the creation of a shared emotional sense through various interpersonal processes. It is argued that ignoring these factors can lead to pragmatic theories which overestimate the processing demands facing the conversationalist, and that this overestimation will be problematic for any systems (...) which seek to build on these theories in the future. (shrink)

Although Artificial Intelligence (AI) and Machine Learning (ML) are attracting a lot of scientific and engineering attention nowadays, nothing up to now has been achieved to reach the level of building machines that possess human-like intelligence. Though, the engineering community continuously claims that several engineering problems are solved using AI or ML. Here, it is argued that engineers are not being able to build intelligent machines, implying that the systems claimed to have AI/ML belong to different engineering domains. The base (...) of the syllogism is the existence of four main obstacles on which extensive elucidation is performed. In addition, attempt to clear out the developed confusion, mis-use and ab-use of the phrases “Artificial Intelligence” and “Machine Learning” by scientists and engineers is carried out. Furthermore, mathematical, and philosophical approaches are also mentioned that strengthen the argument against AI implementability as part of the whole syllogism. Finally, an alternative approach (not being unique) is suggested and discussed for performing research on AI and ML by the engineers. It is based on complexity theory and non-linear adaptive systems and provides the benefit of eliminating the before mentioned pragmatic and philosophical obstacles that engineers are facing and ignoring, without creating confusion on this scientific endeavor. (shrink)

Argumentation schemes [1–3] are a relatively recent notion that continues an extremely ancient debate on one of the foundations of human reasoning, human comprehension, and obviously human argumentation, i.e., the topics. To understand the revolutionary nature of Walton’s work on this subject matter, it is necessary to place it in the debate that it continues and contributes to, namely a view of logic that is much broader than the formalistic perspective that has been adopted from the 20th century until nowadays. (...) With his book Argumentation schemes for presumptive reasoning, Walton attempted to start a dialogue between three different fields or views on human reasoning – one (argumentation theory) very recent, one (dialectics) very ancient and with a very long tradition, and one (formal logic) relatively recent, but dominating in philosophy. Argumentation schemes were proposed as dialectical instruments, in the sense that they represented arguments not only as formal relations, but also as pragmatic inferences in the sense that they depend on what the interlocutors share and accept in a given dialogical circumstance and affect their dialogical relation. In this introduction, the notion of argumentation scheme will be analyzed in detail, showing its different dimensions and its defining features which make them an extremely useful instrument in Artificial Intelligence. This theoretical background will be followed by a literature review on the uses of the schemes in computing, aimed at identifying the most important areas and trends, the most promising proposals, and the directions of future research. (shrink)

Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical (...) challenges. Nevertheless, pragmatic problem-solving demands that things should be sorted so that their grouping will promote successful actions for some specific end. In this article, we review and compare previous attempts to classify AI systems for the purpose of implementing AI governance in practice. We find that attempts to classify AI systems proposed in previous literature use one of three mental models: _the Switch_, i.e., a binary approach according to which systems either are or are not considered AI systems depending on their characteristics; _the Ladder_, i.e., a risk-based approach that classifies systems according to the ethical risks they pose; and _the Matrix_, i.e., a multi-dimensional classification of systems that take various aspects into account, such as context, input data, and decision-model. Each of these models for classifying AI systems comes with its own set of strengths and weaknesses. By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the vocabulary needed to demarcate the material scope of their AI governance frameworks. (shrink)

Fairness of Artificial Intelligence decisions has become a big challenge for governments, companies, and societies. We offer a theoretical contribution to consider AI ethics outside of high-level and top-down approaches, based on the distinction between “reality” and “world” from Luc Boltanski. To do so, we provide a new perspective on the debate on AI fairness and show that criticism of ML unfairness is “realist”, in other words, grounded in an already instituted reality based on demographic categories produced by institutions. Second, (...) we show that the limits of “realist” fairness corrections lead to the elaboration of “radical responses” to fairness, that is, responses that radically change the format of data. Third, we show that fairness correction is shifting to a “domination regime” that absorbs criticism, and we provide some theoretical and practical avenues for further development in AI ethics. Using an ad hoc critical space stabilized by reality tests alongside the algorithm, we build a shared responsibility model which is compatible with the radical response to fairness issues. Finally, this paper shows the fundamental contribution of pragmatic sociology theories, insofar as they afford a social and political perspective on AI ethics by giving an active role to material actors such as database formats on ethical debates. In a context where data are increasingly numerous, granular, and behavioral, it is essential to renew our conception of AI ethics on algorithms in order to establish new models of responsibility for companies that take into account changes in the computing paradigm. (shrink)

In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity of the term “explanation” in explainable AI (XAI) can be solved by adopting any of four different extant accounts of explanation in the philosophy of science: the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New Mechanist models. In this chapter, I show that the authors’ claim that these accounts can be applied to deep neural networks as they would to any natural phenomenon is mistaken. I also (...) provide a more general argument as to why the notion of explainability as it is currently used in the XAI literature bears little resemblance to the traditional concept of scientific explanation. It would be more fruitful to use the label “understandable AI” to avoid the confusion that surrounds the goal and purposes of XAI. In the second half of the chapter, I argue for a pragmatic conception of understanding that is better suited to play the central role attributed to explanation in XAI. Following De Regt (2017) and Kuorikoski and Ylikoski (2015), the conditions of satisfaction for understanding an ML system are fleshed out in terms of an agent’s success in using the system. (shrink)

We argue that the concepts of mechanism and autonomy appear to be antagonistic when autonomy is conflated with agency. Once these concepts are disentangled, it becomes clearer how autonomy emerges from complex forms of control. Subsequently, current biomimetic strategies tend to focus on homeostatic regulatory systems; we propose that research in AI and robotics would do well to incorporate biomimetic strategies that instead invoke models of allostatic mechanisms as a way of understanding how to enhance autonomy in artificial systems.

This paper supports the view that the ongoing shift from orthodox to embodied-embedded cognitive science has been significantly influenced by the experimental results generated by AI research. Recently, there has also been a noticeable shift toward enactivism, a paradigm which radicalizes the embodied-embedded approach by placing autonomous agency and lived subjectivity at the heart of cognitive science. Some first steps toward a clarification of the relationship of AI to this further shift are outlined. It is concluded that the success of (...) enactivism in establishing itself as a mainstream cognitive science research program will depend less on progress made in AI research and more on the development of a phenomenological pragmatics. (shrink)

This paper presents some considerations about the relationship between languages and computer systems from a pragmatic, user-centered point of view.

Within AI and the cognitively related disciplines, there exist a multiplicity of uses of belief. On the face of it, these differing uses reflect differing views about the nature of an objective phenomenon called belief. In this paper I distinguish six distinct ways in which belief is used in AI. I shall argue that not all these uses reflect a difference of opinion about an objective feature of reality. Rather, in some cases, the differing uses reflect differing concerns with special (...) AI applications. In other cases, however, genuine differences exist about the nature of what we pre-theoretically call belief. To an extent the multiplicity of opinions about, and uses of belief, echoes the discrepant motivations of AI researchers. The relevance of this discussion for cognitive scientists and philosophers arises from the fact that (a) many regard theoretical research within AI as a branch of cognitive science, and (b) even if theoretical AI is not cognitive science, trends within AI influence theories developed within cognitive science. It should be beneficial, therefore, to unravel the distinct uses and motivations surrounding belief, in order to discover which usages merely reflect differing pragmatic concerns, and which usages genuinely reflect divergent views about reality. (shrink)

This contribution presents two perspectives: one based on AI expertise and the other on the importance of knowledge as human knowledge. The later focuses on the purpose of education by revisiting Alexander von Humboldt’s concept of Bildung as the main purpose of education. Resisting the shifting to radically pragmatic models of education, without Bildung, in recent decades, in the Global North which develops a system of disposable education that focuses on training a workforce for the market. Concerned by the production (...) of disposable knowledge and assessments, with the sole goal of achieving good grades and securing employment, we see Generative AI, as mirroring this functionalist and economist vision of society, revealing strongly the limits of this paradigm. This article first explains how automatic learning, the foundation of generative AI, works. Second, we shall discuss human deep learning, as opposed to surface learning showing how it is required to develop as a person in the Bildung perspective. Third, we highlight this first intuition that AI reveals the functioning of our societies and move beyond, with an art of questioning in philosophy. (shrink)

This book (in Italian) provides an introduction to the debate about the distinction between semantics and pragmatics, starting with the work of Paul Grice, and touching on some of the most important authors and theories in the literature, including truth-conditional pragmatics, semantic minimalism, indexical and non-indexical contextualism.

In this Interview, Professor Robert B. Brandom answered ten detailed questions about his philosophy of Rational Pragmatism and Semantic Expressivism, grouped into four topics. 1. Metaphysics and Anthropology, 2. Pragmatics and Semantics, 3. Epistemic Expressivism and 4. Philosophy of Logic. With his careful answers Professor Brandom offers many additional insights into his rigorously constructed account of the relationship “between what we say and think, and what we are saying and thinking about” around the human practice of asking for and (...) giving reasons. A final, additional question pointed at a principal motivation for putting together the present issue: how to reconcile Wittgenstein’s assertion that philosophy must not proffer any theories with the very explicit system of explanations Brandom has constructed. This same issue is addressed to some extent already in Professor Brandom’s new article contained in this issue, but his answer, asserting that he does not proffer a theory but only makes explicit what is already there, might be seen as an unambiguous statement of the continuous presence of a contested Wittgensteinian principle in Brandom’s work. (shrink)

Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of (...) language (in reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored. (shrink)

Erroneously attributing propositional attitudes (desires, beliefs...) to computational artefacts has become internationally commonplace in the public arena, especially amongst the new generation of non-initiated users. Technology for rendering machines “user-friendly” is often inspired by interpersonal human communication. This calls forth designers to conceptualise a major component of human intelligence: the sense ofcommunicability, and its logical consequences. The inherentincommunicability of machines subsequently causes a shift in design strategy. Though cataloguing components of bouts between person and machine with Speech Act Theory has (...) been popular, I will endeavour to present thesine qua non for their insertion into a larger unit of discourse — their societal embodiment. I shall argue that the so-called “intelligence” of the artificial should to be seenas a purposeful act that is socially generated, because it comes of Man,for Man. Designership will provide the forum for evolving user requirements and interface renewal. (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)

Over the years, AI has undergone a transformation from its original aim of producing an ‘intelligent’ machine to that of producing pragmatic solutions of problems of the market place. In doing so, AI has made a significant contribution to the debate on whether the computer is an instrument or an interlocutor. This paper discusses issues of problem solving and creativity underlying this transformation, and attempts to clarify the distinction between theresolutive intelligence andproblematic intelligence. It points out that the advance of (...) ‘intelligent’ technology, with its failure to make a clear distinction betweenresolutive andcreative intelligence, could contribute to the further cultural marginalisation of human activities not connected with production. A further danger is that AI products may suffer a further loss of social reputation and prestige for those activities for which it is not possible to build artificial devices. (shrink)

Could an artificial intelligence become a legal person? As of today, this question is only theoretical. No existing computer program currently possesses the sort of capacities that would justify serious judicial inquiry into the question of legal personhood. The question is nonetheless of some interest. Cognitive science begins with the assumption that the nature of human intelligence is computational, and therefore, that the human mind can, in principle, be modelled as a program that runs on a computer. Artificial intelligence (AI) (...) research attempts to develop such models. But even as cognitive science has displaced behavioralism as the dominant paradigm for investigating the human mind, fundamental questions about the very possibility of artificial intelligence continue to be debated. This Essay explores those questions through a series of thought experiments that transform the theoretical question whether artificial intelligence is possible into legal questions such as, "Could an artificial intelligence serve as a trustee?" What is the relevance of these legal thought experiments for the debate over the possibility of artificial intelligence? A preliminary answer to this question has two parts. First, putting the AI debate in a concrete legal context acts as a pragmatic Occam's razor. By reexamining positions taken in cognitive science or the philosophy of artificial intelligence as legal arguments, we are forced to see them anew in a relentlessly pragmatic context. Philosophical claims that no program running on a digital computer could really be intelligent are put into a context that requires us to take a hard look at just what practical importance the missing reality could have for the way we speak and conduct our affairs. In other words, the legal context provides a way to ask for the "cash value" of the arguments. The hypothesis developed in this Essay is that only some of the claims made in the debate over the possibility of AI do make a pragmatic difference, and it is pragmatic differences that ought to be decisive. Second, and more controversially, we can view the legal system as a repository of knowledge-a formal accumulation of practical judgments. The law embodies core insights about the way the world works and how we evaluate it. Moreover, in common-law systems judges strive to decide particular cases in a way that best fits the legal landscape-the prior cases, the statutory law, and the constitution. Hence, transforming the abstract debate over the possibility of AI into an imagined hard case forces us to check our intuitions and arguments against the assumptions that underlie social decisions made in many other contexts. By using a thought experiment that explicitly focuses on wide coherence, we increase the chance that the positions we eventually adopt will be in reflective equilibrium with our views about related matters. In addition, the law embodies practical knowledge in a form that is subject to public examination and discussion. Legal materials are published and subject to widespread public scrutiny and discussion. Some of the insights gleaned in the law may clarify our approach to the artificial intelligence debate. (shrink)

Offering a novel and pragmatic perspective, this timely book critically examines the development of a culture of machinist regulation and questions whether this approach is appropriate in an era of rising biological technologies. Adopting an ontological approach, James Griffin considers how current regulatory frameworks favour digital technology and how this may change in the future. Griffin adeptly investigates how regulation can impact the nature of new technologies, especially as biological computing is becoming more commonplace. Chapters provide a wealth of critical (...) analysis, considering cutting-edge technologies such as AI, prosthesis, and biological computing. Griffin outlines a proposed reformative system which focuses on the biological substrate in the creation of cultural works. The book serves to highlight the ever-increasing need for awareness of the importance of biological substrates and for a regulatory system which reflects this. The State of Cultural Biology will be an essential read for academics and students interested in intellectual property law, law and technology, legal philosophy and law's role in society. It will also prove invaluable to policymakers and professionals looking to broaden their knowledge on the regulation of modern technology. (shrink)

Many aquatic invasive species (AIS) management programs are doing important work on preventing non-indigenous species movement to our wild places. Attitudes and perspectives on aquatic non-indigenous species and their management by ecologists and the public are fundamentally a question of human values. Despite eloquent philosophical writings on treatment of non-indigenous species, management agency rhetoric on ‘invasive’ species usually degenerates to a good versus evil language, often with questionable results and lost conservation dollars. We assess and learn from an established AIS (...) program. We discuss an ethic framework and operational directives to minimise the trap of a binary classification of species into bad or good, and we advocate for a principled pragmatic approach to minimise conflicts. We make a case for not labelling species and instead focusing on managing nuisance conditions and protecting ecosystem health. (shrink)

Powerful transformer models based on neural networks such as GPT-4 have enabled huge progress in natural language processing. This paper identifies three challenges for computer programs dealing with metaphors. First, the phenomenon of Twice-Apt-Metaphors shows that metaphorical interpretations do not have to be triggered by syntactical, semantic or pragmatic tensions. The detection of these metaphors seems to involve a sense of aesthetic pleasure or a higher-order theory of mind, both of which are difficult to implement into computer programs. Second, the (...) contexts relative to which metaphors are interpreted are not simply given but must be reconstructed based on pragmatic considerations that can involve presuppositional pretence. If computer programs cannot produce or understand such a form of pretence, they will have problems dealing with certain metaphors. Finally, adequately interpreting and reacting to some metaphors seems to require the ability to have internal, first-personal experiential and affective states. Since it is questionable whether computer programs have such mental states, it can be assumed that they will have problems with these kinds of metaphors. (shrink)

At the turn of the 21st century, Susan Leigh Anderson and Michael Anderson conceived and introduced the Machine Ethics research program, that aimed to highlight the requirements under which autonomous artificial intelligence systems could demonstrate ethical behavior guided by moral values, and at the same time to show that these values, as well as ethics in general, can be representable and computable. Today, the interaction between humans and AI entities is already part of our everyday lives; in the near future (...) it is expected to play a key role in scientific research, medical practice, public administration, education and other fields of civic life. In view of this, the debate over the ethical behavior of machines is more crucial than ever and the search for answers, directions and regulations is imperative at an academic, institutional as well as at a technical level. Our discussion with the two inspirers and originators of Machine Ethics highlights the epistemological, metaphysical and ethical questions arising by this project, as well as the realistic and pragmatic demands that dominate artificial intelligence and robotics research programs. Most of all, however, it sheds light upon the contribution of Susan and Michael Anderson regarding the introduction and undertaking of a main objective related to the creation of ethical autonomous agents, that will not be based on the “imperfect” patterns of human behavior, or on preloaded hierarchical laws and human-centric values. (shrink)

Agenda Relevance is the first volume in the authors' omnibus investigation of the logic of practical reasoning, under the collective title, A Practical Logic of Cognitive Systems. In this highly original approach, practical reasoning is identified as reasoning performed with comparatively few cognitive assets, including resources such as information, time and computational capacity. Unlike what is proposed in optimization models of human cognition, a practical reasoner lacks perfect information, boundless time and unconstrained access to computational complexity. The practical reasoner is (...) therefore obliged to be a cognitive economizer and to achieve his cognitive ends with considerable efficiency. Accordingly, the practical reasoner avails himself of various scarce-resource compensation strategies. He also possesses neurocognitive traits that abet him in his reasoning tasks. Prominent among these is the practical agent's striking (though not perfect) adeptness at evading irrelevant information and staying on task. On the approach taken here, irrelevancies are impediments to the attainment of cognitive ends. Thus, in its most basic sense, relevant information is cognitively helpful information. Information can then be said to be relevant for a practical reasoner to the extent that it advances or closes some cognitive agenda of his. The book explores this idea with a conceptual detail and nuance not seen the standard semantic, probabilistic and pragmatic approaches to relevance; but wherever possible, the authors seek to integrate alternative conceptions rather than reject them outright. A further attraction of the agenda-relevance approach is the extent to which its principal conceptual findings lend themselves to technically sophisticated re-expression in formal models that marshal the resources of time and action logics and label led deductive systems. Agenda Relevance is necessary reading for researchers in logic, belief dynamics, computer science, AI, psychology and neuroscience, linguistics, argumentation theory, and legal reasoning and forensic science, and will repay study by graduate students and senior undergraduates in these same fields. Key features: relevance action and agendas practical reasoning belief dynamics non-classical logics labelled deductive systems. (shrink)

Research in artificial intelligence (AI) has led to revise the challenges of the AI initial programme as well as to keep us alert to peculiarities and limitations of human cognition. Both are linked, as a careful further reading of the Turing’s test makes it clear from Searle’s Chinese room apologue and from Dreyfus’ suggestions, and in both cases, ideal had to be turned into operating mode. In order to rise these more pragmatic challenges AI does not hesitate to link together (...) operations of various levels and functionalities, more specific or more general. The challenges are not met by an operating formal system which should have from the outset all the learning skills, but -for instance in simulation- by the dynamics of a succession of solutions open to adjustments as well as to reflexive repeats. (shrink)

The term “Contemplative sciences” refers to an interdisciplinary approach to mind that aims at a better understanding of alternative states of consciousness, like those obtained trough deep concentration and meditation, mindfulness and other “superior” or “spiritual” mental states. There is, however, a key discipline missing: artificial intelligence. AI has forgotten its original aims to create intelligent machines that could help us to understand better what intelligence is and is more worried about pragmatical stuff, so almost nobody in the field seems (...) to be interested to join this new effort of contemplative science. In this paper, I would like to accomplish the following: (1) To give a brief description of the field of “contemplative sciences;” (2) To argue why AI should actively join this new paradigm on the study of the mind; and (3) To set up a research program on artificial wisdom: that is to design computational systems that can model at least some relevant aspects of human wisdom. (shrink)

Artificial Intelligence and Philosophy. Artificial Intelligence can be considered as the so far last attempt to decode the anthropological comparison between human beings and machines. Thereby it also represents in a prominent way what can be called "systemic thought". Searle's conclusive argument against strong AI (that is the idea of computers having intention in a literal way) refers to his precise distinction between syntax and semantics. This difference obviously opposing some of Searle's other essential ideas will only convince if it (...) also explains the genetic-pragmatic aspect. A theory explaining the "life of mind" and the possibility of understanding needs to combine representation and intention with the subjective causation of signs. At the same time they have to be contextualized within a model of teleologically interpreted life recognized with the help of self-experience and self-reflection. This suggests that AI is a simulation which wrongly believes to be a real duplication. Actually it is a semiotic reduction (syntax and semantic surface of signs only) and a psychological compensation (Turing test) connected with a genetic or abductive fallacy. The biological decontextualization, in fact the elimination of teleology and intention, the deconstruction of subjectivity, the loss of the genetic-pragmatic dimension and the abductive fallacy induce the strong AI to confuse its surface-illusion of simulated understanding with the real process itself. (shrink)

This article explores how one might best understand Herbert Simon’s work. Many know him as a Nobel Prize-winning economist, an administrative theorist, a founder of AI, or as a psychologist—a man of many identities. The discussion suggests that ‘bounded rationality’ was Simon’s metaphor for the dynamic and pragmatic humanism he stood up against, the ‘rational man’, which he saw as a metaphor for today’s infertile anti-humanist ‘rational choice’ theorizing. Simon’s achievements were vast and exemplary, but they are of particular interest (...) to management theorists. Although Simon was awarded the Nobel Prize in economics, his work stood on a devastating critique of neoclassical economics. The main elements of his reputation are as a theorist of organizational decision-making, cognition, and management. Despite the seeming paradox between Simon’s distrust of human computation and his untroubled faith in computers, the article argues that Simon never abandoned his early humanising pluralism and philosophical positions. (shrink)

Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...) Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realise biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biologically and cognitive accuracy of artificial systems in order project and predict their explanatory power with respect to the natural systems taken as source of inspiration. -/- Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science. (shrink)

This letter explores the intricate historical and contemporary links between large language models (LLMs) and cognitive science through the lens of information theory, statistical language models, and socioanthropological linguistic theories. The emergence of LLMs highlights the enduring significance of information‐based and statistical learning theories in understanding human communication. These theories, initially proposed in the mid‐20th century, offered a visionary framework for integrating computational science, social sciences, and humanities, which nonetheless was not fully fulfilled at that time. The subsequent development of (...) sociolinguistics and linguistic anthropology, especially since the 1970s, provided critical perspectives and empirical methods that both challenged and enriched this framework. This letter proposes that two pivotal concepts derived from this development, metapragmatic function and indexicality, offer a fruitful theoretical perspective for integrating the semantic, textual, and pragmatic, contextual dimensions of communication, an amalgamation that contemporary LLMs have yet to fully achieve. The author believes that contemporary cognitive science is at a crucial crossroads, where fostering interdisciplinary dialogues among computational linguistics, social linguistics and linguistic anthropology, and cognitive and social psychology is in particular imperative. Such collaboration is vital to bridge the computational, cognitive, and sociocultural aspects of human communication and human−AI interaction, especially in the era of large language and multimodal models and human‐centric Artificial Intelligence (AI). (shrink)

An unbiased metaphysics of consciousness allows us to put forward a thesis that con­sciousness is not an object, but a fundamental problem of modern philosophy and differ­ent solutions of this problem define the history of European philosophy. For Descartes, consciousness is Cogito, the personalization of substance by the mind acting in a variety of cogital, conscious acts. Kant, for whom substance is a mode of thinking the unity of experience, Cogito is not a thing (Res), but a transcendental condition for (...) the forma­tion of notions in the process of categorical synthesis. The resurgent phenomenology and analytical philosophy of consciousness in the 21st century aim at studying consciousness, respectively, in the transcendental-theoretical and pragmatic-naturalistic settings. Contin­uing the Kantian line of thinking, one can see the processuality of consciousness as data acquisition process, but the verb formed from a noun “consciousness” (for this processu­ality) is difficult to adequately translate into English. To achieve completeness in identi­fying the problem area of the philosophy of consciousness, it is necessary to take into ac­count one more type of conscious activity referred to as “osoznanie” in Russian (“aware­ness”, “coming into consciousness about something”). The Russian verb formed from the noun Smysl cannot be adequately translated into English as the main language of the ana­lytical philosophy of consciousness and this term can mean the opposite of data acquisi­tion process and can refer us to the idea of Integrity (A.V. Smirnov). Once the most im­portant, meaningful and existential activity of the mind is identified as the denotation of this term, the problem of Smysl acquires the potential of changing the perspective on the possibility of creating a “strong” AI. (shrink)

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties (...) of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor) . (shrink)

Unsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date. This stands in stark contrast to supervised and reinforcement learning algorithms, which have been widely studied and critically evaluated, often with an emphasis on ethical concerns. In this article, I analyze three canonical unsupervised learning problems: clustering, abstraction, and generative modeling. I argue that these methods raise unique epistemological and (...) ontological questions, providing data-driven tools for discovering natural kinds and distinguishing essence from contingency. This analysis goes some way toward filling the lacuna in contemporary philosophical discourse on unsupervised learning, as well as bringing conceptual unity to a heterogeneous field more often described by what it isnot(i.e., supervised or reinforcement learning) than by what itis. I submit that unsupervised learning is not just a legitimate subject of philosophical inquiry but perhaps the most fundamental branch of all AI. However, an uncritical overreliance on unsupervised methods poses major epistemic and ethical risks. I conclude by advocating for a pragmatic, error-statistical approach that embraces the opportunities and mitigates the challenges posed by this powerful class of algorithms. (shrink)

This paper discusses the interrelationship between wisdom, science and craft from the perspective of the Wittgenstein concept of tacit knowledge. It challenges the notion of the ‘rules-model’ as put forward by Logical Positivists, and shows the limitation of this model for describing the tacit dimension of knowledge. The paper demonstrates the crucial role of practice in ‘rule-following’ in the real world. It is held that ‘to follow a rule’ is to practice a custom, a usage or an institutional practice. Hence, (...) rules can only exist as a link in social life.The dream of the ‘precise’ language can only be realised in a closed scientific world. It is inadequate for reflecting the user's relationship to language and its content and practice in varied ‘use situations’. It is only through examples that we learn to deal with, describe, interpret and learn from the new situations.The rule-based models which are used to acquire and describe human knowledge in syntactic and propositional forms are, in effect, an impoverishment of the description of reality. It is argued that expert knowledge and linguistic knowledge are linked together and emerge as two sides of the same subject as the pragmatic perspective of reality. The tacit dimension of expert knowledge is, in many cases, more significant than the linguistic knowledge, especially in the case of the vocational and aesthetic world.The challenge to AI researchers, therefore, is to recognise that knowledge based systems which ignore the tacit dimension of expert knowledge not only distance the user from reality but also impoverish the learning process itself. (shrink)

Se la tradizione continentale, da Nietzsche a Foucault, ha sottolineato le implicazioni critiche e destabilizzanti del metodo genealogico, gli approcci analitici hanno dimostrato che esso può anche offrire sostegno ai propri oggetti di indagine: norme comportamentali, pratiche, concetti e così via. Un resoconto genealogico, per esempio, può avere un carattere giustificativo quando individua una relazione funzionale necessaria tra il concetto o la pratica in esame e bisogni umani abbastanza fondamentali. La questione che voglio affrontare riguarda le presunte capacità giustificative di (...) questo tipo di spiegazioni genealogiche. In che misura modificano davvero lo spazio delle ragioni? Sostengo che la loro portata normativa e valutativa è variabile e che l'esame di alcuni casi limite aiuta a dimostrarlo. Più specificamente, ritengo che in alcuni casi le ragioni offerte attraverso queste spiegazioni siano ragioni non tanto per giustificare l'uso di certi concetti o pratiche, quanto per giustificare coloro che li vivono. (shrink)