Ioannis G. Ntagkas

We conducted two experiments to determine whether common folk think that so-called _tell-tale sign_ bald-faced lies are intended to deceive—since they have not been tested before. These lies involve tell-tale signs (e.g. blushing) that show that the speaker is lying. Our study was designed to avoid problems earlier studies raise (these studies focus on a kind of bald-faced lie in which supposedly everyone knows that what the speaker says is false). Our main hypothesis was that the participants will think that the protagonists from our examples lied without intending to deceive, and the results of our surveys confirmed this hypothesis: most of our participants rated tell-tale sign lies as lies not intended to deceive. Therefore, our analysis suggests that common folk think that some lies are not intended to deceive.

This article defends the view that liars need not intend to deceive. I present common objections to this view in detail and then propose a case of a liar who can lie but who cannot deceive in any relevant sense. I then modify this case to get a situation in which this person lies intending to tell his hearer the truth and he does this by way of getting the hearer to recognize his intention to tell the truth by lying. This case, and further cases that I develop from it, demonstrate that lying without the intention to deceive is possible.

A number of arguments purport to show that vague properties determine sharp boundaries at higher orders. That is, although we may countenance vagueness concerning the location of boundaries for vague predicates, every predicate can instead be associated with precise knowable cut-off points deriving from precision in their higher order boundaries. I argue that this conclusion is indeed paradoxical, and identify the assumption responsible for the paradox as the Brouwerian principle B for vagueness: that if p then it's determinate that it's not determinate that not p. Other paradoxes which do not appear to turn on B turn instead on some subtle issues concerning the relation between assertion, belief and higher order vagueness. In this paper a theory of assertion, knowledge and logic is outlined which allows one to avoid any kind of higher order precision. A class of realistic models containing counterexamples to B and a number of weakenings of B are introduced and its logic is shown to support vagueness at every order.

Virtue epistemology is a diverse and flourishing field, one of the most exciting developments in epistemology to emerge over the last three decades. Virtue epistemology begins with the premise that epistemology is a normative discipline and, accordingly, a central task of epistemology is to explain the sort of normativity that knowledge, justified belief, and the like involve. A second premise is that a focus on the intellectual virtues (individual intellectual excellences) is essential to carrying out this central task. This collection offers some of the most influential and agenda-setting work at the heart of virtue epistemology's research program. Taken together, they will equip the reader to enter the ongoing discussion and debate in the field. The selections range from seminal contributions by Ernest Sosa, who introduced the notion of intellectual virtue into the contemporary literature, to a study of "epistemic justice" that draws on _To Kill a Mockingbird_ and The Talented Mr. Ripley. The readings include overviews of the field that also serve to advance the discussion; investigations of the nature of knowledge; reflections on the value of knowledge; examinations of credit and luck; and explorations of future directions for research. The hardcover edition does not include a dust jacket.

EPISTEMOLOGY “This is a superb companion to Epistemology: An Anthology. It consists of sixty commentaries, one for each of the sixty entries in that anthology. Turri is an extremely lucid writer, with a wonderful knack for finding and laying out argumentative structure, and for explaining crucial concepts. His commentary will greatly aid student comprehension and enhance class discussion.” Ernest Sosa, Rutgers University “Turri’s discussions are engaging and lucid. They are written for beginning students and will serve that purpose beautifully, but they are so well done that even veteran epistemologists will find them helpful.” John Greco, Saint Louis University Epistemology: A Guide is a straightforward and accessible introduction to contemporary epistemology for those studying the topic for the first time. It introduces and explains the main arguments of the most influential publications in the field from the last 50 years. Balancing simplicity of argument with accuracy and detail, this guide covers the central topics of theory of knowledge, including skepticism, epistemic justification, epistemic closure, virtue epistemology, and naturalized epistemology. Instead of artificially treating themes in isolation, it provides a clear context for key topics and concepts. Designed to stand alone or to accompany the second edition of Epistemology: An Anthology (Wiley Blackwell, 2008), this is a deft and concise introduction to a foundational topic in philosophy.

Fully updated with new topics covering the latest developments and debates, the second edition of this highly influential text retains its unique combination of accessibility and originality. Second edition of a highly influential text that has already become a standard in the field, for students and professional researchers alike, due to its impressive line-up of contributors, and its unique combination of accessibility and originality Twenty-six essays in total, covering 13 essential topics Features five new topics that bring readers up to speed on some of the latest developments in the field, and give them a glimpse of where it's headed: Should knowledge come first? Do practical matters affect whether you know? Is virtuous motivation essential to knowing? Can knowledge be lucky? Can evidence be permissive? Substantially updates two other debates: Is there immediate justification? Can belief be justified through coherence alone?

Traditional theories of knowledge explain knowledge in terms of things like justification and belief. Knowledge-first theories of justification and belief explain justification and belief in terms of knowledge. When epistemologists ask whether knowledge "comes first," they are asking whether traditional theories of knowledge take the right approach, or whether knowledge-first theories of things like justification and belief take the right approach. In her contribution to this debate, Mona Simion defends knowledge-first epistemology by defending a knowledge-first theory of belief and using this theory of belief to motivate a knowledge-first theory of justification. In his contribution to this debate, Aidan McGlynn defends traditional epistemology by critiquing Simion's knowledge-first theory of belief. As Simion and McGlynn both recognize, Simion's argument depends on the idea that beliefs that do not amount to knowledge are epistemically defective. While Simion thinks this is plausible, McGlynn thinks we can produce examples of epistemically non-defective beliefs that do not amount to knowledge. As McGlynn acknowledges, his contribution to the debate does not provide a traditional theory of knowledge in place of Simion's knowledge-first theories of justification and belief. However, if McGlynn is right that we can produce examples of epistemically non-defective beliefs that do not amount to knowledge, this casts doubt on at least one central idea in knowledge-first epistemology: that knowledge has explanatory priority over belief.

Although Kant envisaged a prominent role for logic in the argumentative structure of his Critique of pure reason [12], logicians and philosophers have generally judged Kant’s logic negatively. What Kant called ‘general’ or ‘formal’ logic has been dismissed as a fairly arbitrary subsystem of first order logic, and what he called ‘transcendental logic’ is considered to be not a logic at all: no syntax, no semantics, no definition of validity. Against this, we argue that Kant’s ‘transcendental logic’ is a logic in the strict formal sense, albeit with a semantics and a definition of validity that are vastly more complex than that of first order logic. The main technical application of the formalism developed here is a formal proof that Kant’s Table of Judgements in §9 of the Critique of pure reason, is indeed, as Kant claimed, complete for the kind of semantics he had in mind. This result implies that Kant’s ’general’ logic is after all a distinguished subsystem of first order logic, namely what is known as geometric logic.

The central claim of normative pragmatism is that intentional states can be explained in terms of participation in practices. My aim in this paper is not so much to defend this claim as to rearticulate it in a different medium: the medium of computation. I describe two computer programs in which this claim is re-expressed. The first is the latest version of THE SIMS, in which participation in practices enables the Sims to do and understand more. The second is a prototype simulation of philosophical debate. In this second program, normative pragmatism is expressed at two different levels: once as the theory powering the implementation, and once as a set of claims which are debated by the simulated philosophers. Here, a philosophical theory is used to implement a system in which that very same theory is articulated, challenged, and justified.

We are able to participate in countless different sorts of social practice. This indefinite set of capacities must be explainable in terms of a finite stock of capacities. This paper compares and contrasts two different explanations. A standard decomposition of the capacity to participate in social practices goes something like this: the interpreter arrives on the scene with a stock of generic practice-types. He looks at the current scene to fill-in the current tokens of these types. He looks at the current state of these practice tokens to see what actions are available to him. He uses his current desires to choose between these various possible actions. I argue that this standard explanation is defective, drawing on arguments by Searle and Wittgenstein and Garfinkel. I propose an alternative explanation, in which the participants must continually show each other the state of the scene in order to maintain the scene’s intelligibility. I provide a simple formal language in which to describe this alternative approach, in which we can state quite precisely what someone is doing when they participate in a practice. This language is related to both deontic and epistemic logics, but it is much simpler – it does not include the classic propositional connectives, and it is driven by a very different set of assumptions. The inspirations for this formal language are Searle’s analysis of directions of fit, Wittgenstein’s remarks on rule-following and Garfinkel’s ethnomethodology.

Research in multi-agent systems typically assumes a regulative model of social practice. This model starts with agents who are already capable of acting autonomously to further their individual ends. A social practice, according to this view, is a way of achieving coordination between multiple agents by restricting the set of actions available. For example, in a world containing cars but no driving regulations, agents are free to drive on either side of the road. To prevent collisions, we introduce driving regulations, insisting that everyone drives on the left hand side of the road. We accept this limitation on our freedom because it lowers the probability of a collision. This paper describes AI systems that are based on the constitutive view of social practice. According to this alternative view, there are certain actions that are only available to the agent because he is participating in a practice of a certain sort. For example, you can swing a peculiarly shaped piece of wood without participating in any particular practice - but this action will only constitute a strike if you are participating in a game of baseball. I can raise my hand whenever I like, but this only counts as voting for the motion within the institution of voting. But not all the examples are from frivolous, optional or institutional activities. Sellars and Brandom argue that even the fundamental act of assertion is only possible within the context of a particular social practice: the Game of Giving and Asking for Reasons. The constitutive view, which is familiar to philosophers, is relatively unknown within the AI community. The central aim of this paper is to show how the constitutive view of practice can be put to use in AI applications.

In this paper, I reinterpret Kant’s Transcendental Analytic as a description of a cognitive architecture. I describe a computer implementation of this architecture, and show how it has been applied to two unsupervised learning tasks. The resulting program is very data efficient, able to learn from a tiny handful of examples. I show how the program achieves data-efficiency: the constraints described in the Analytic of Principles are reinterpreted as strong prior knowledge, constraining the set of possible solutions.

This paper formalizes part of the cognitive architecture that Kant develops in the Critique of Pure Reason. The central Kantian notion that we formalize is the rule. As we interpret Kant, a rule is not a declarative conditional stating what would be true if such and such conditions hold. Rather, a Kantian rule is a general procedure, represented by a conditional imperative or permissive, indicating which acts must or may be performed, given certain acts that are already being performed. These acts are not propositions; they do not have truth-values. Our formalization is related to the input/ output logics, a family of logics designed to capture relations between elements that need not have truth-values. In this paper, we introduce KL3 as a formalization of Kant’s conception of rules as conditional imperatives and permissives. We explain how it differs from standard input/output logics, geometric logic, and first-order logic, as well as how it translates natural language sentences not well captured by first-order logic. Finally, we show how the various distinctions in Kant’s much-maligned Table of Judgements emerge as the most natural way of dividing up the various types and sub-types of rule in KL3. Our analysis sheds new light on the way in which normative notions play a fundamental role in the conception of logic at the heart of Kant’s theoretical philosophy.

This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws – must be integrated into a coherent whole. On our account, making sense of sensory input is a type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. In fact, it is able to do all three tasks simultaneously. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence.

How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task [1]. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure. However, the original formulation of the apperception task had one fundamental limitation: it assumed the raw sensory input had already been parsed using a set of discrete categories, so that all the system had to do was receive this already-digested symbolic input, and make sense of it. But what if we don't have access to pre-parsed input? What if our sensory sequence is raw unprocessed information? The central contribution of this paper is a neuro-symbolic framework for distilling interpretable theories out of streams of raw, unprocessed sensory experience. First, we extend the definition of the apperception task to include ambiguous (but still symbolic) input: sequences of sets of disjunctions. Next, we use a neural network to map raw sensory input to disjunctive input. Our binary neural network is encoded as a logic program, so the weights of the network and the rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules).

In this paper, I attempt to address a fundamental challenge for machine intelligence: to understand whether and how a machine’s internal states and external outputs can exhibit original non-derivative intentionality. This question has three aspects. First, what does it take for a machine to exhibit original de dicto intentionality? Second, what does it take to exhibit original de re intentionality? Third, what is required for the machine to defer to the external objective world by respecting the word-to-world direction of fit? I attempt to answer the first challenge by providing a constitutive counts-as understanding of de dicto intentionality. This analysis involves repurposing Kant’s vision of a self-legislating agent as a specification of a machine that reprograms itself. I attempt to answer the second and third challenges by extending Kant’s synchronic model of de dicto intentionality with Brandom’s interpretation of Hegel’s diachronic model of de re intentionality, using Hegel’s notion of recollection to provide an understanding of what is involved in achieving deference to the external world.

The psychology of belief in the paranormal has often been used to stigmatize believers but it has also been used with a more open-minded approach. This paper describes some research of this kind in which believers were found to report more mystical experience, have more creative personalities, report more manic and depressive experience, and more magical ideation, unwittingly suggesting a link with bipolar disorder and schizotypal personality. In addition, however, these six variables were all found to correlate positively and significantly with each other. The positive manifold suggested a single underlying factor, which indeed emerged from principal components analysis. It was speculated that the underlying factor was “transliminality”—a gating mechanism modulating the flow of information and affect from subliminal levels into supraliminal. Later studies have not as clearly confirmed the existence of the positive manifold but nevertheless the concept of transliminality may be a useful one for understanding paranormal experience and hence paranormal belief

Carnap's position on mathematical truth in The Logical Syntax of Language has been attacked from two sides: Kreisel argues that it is formalistic but should not be, and Friedman argues that it is not formalistic but needs to be. In this paper I argue that the Carnap of Syntax does not eliminate our ordinary notion of mathematical truth in favour of a formal analogue; so Carnap's notion of mathematical truth is not formalistic. I further argue that there is no conflict between Carnap's use of informal notions and his principle of tolerance; so Carnap's definition of mathematical truth need not be formalistic. CiteULike    Connotea    Del.icio.us    What's this?

Clinical ethicists encounter the most emotionally eviscerating medical cases possible. They struggle to facilitate resolutions founded on good reasoning embedded in compassionate care. This book fills the considerable gap between current texts and the continuing educational needs of those actually facing complex ethics consultations in hospital settings. 28 richly detailed cases explore the ethical reasoning, professional issues, and the emotional aspects of these impossibly difficult consultations. The cases are grouped together by theme to aid teaching, discussion and professional growth. The cases inform any reader who has a keen interest in the choices made in real-life medical dilemmas as well as the emotional cost to those who work to improve the situations. On a more advanced level, this book should be read by ethics committee members who participate in ethics consultations, individual ethics consultants, clinicians who seek education about complex clinical ethics cases, and bioethics students

Recent controversies about the level of replicability of behavioral research analyzed using statistical inference have cast interest in developing more efficient techniques for analyzing the results of psychological experiments. Here we claim that complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather than inference. We also highlight some potential pitfalls resulting from adoption of Machine Learning based experiment analysis. If not properly used it can lead to over-optimistic accuracy estimates similarly observed using statistical inference. Remedies to such pitfalls are also presented such and building model based on cross validation and the use of ensemble models. ML models are typically regarded as black boxes and we will discuss strategies aimed at rendering more transparent the predictions.

The extended mind thesis maintains that while minds may be centrally located in one?s brain-and-body, they are sometimes partly constituted by tools in our environment. Critics argue that we have no reason to move from the claim that cognition is embedded in the environment to the stronger claim that cognition can be constituted by the environment. I will argue that there are normative reasons, both scientific and ethical, for preferring the extended account of the mind to the rival embedded account. nema

Autonomy has been central to moral and political philosophy for millenia, and has been positioned as a critical aspect of both justice and wellbeing. Research in psychology supports this position, providing empirical evidence that autonomy is critical to motivation, personal growth and psychological wellness. Responsible AI will require an understanding of, and ability to effectively design for, human autonomy (rather than just machine autonomy) if it is to genuinely benefit humanity. Yet the effects on human autonomy of digital experiences are neither straightforward nor consistent, and are complicated by commercial interests and tensions around compulsive overuse. This multi-layered reality requires an analysis that is itself multidimensional and that takes into account human experience at various levels of resolution. We borrow from HCI and psychological research to apply a model (“METUX”) that identifies six distinct spheres of technology experience. We demonstrate the value of the model for understanding human autonomy in a technology ethics context at multiple levels by applying it to the real-world case study of an AI-enhanced video recommender system. In the process we argue for the following three claims: 1) There are autonomy-related consequences to algorithms representing the interests of third parties, and they are not impartial and rational extensions of the self, as is often perceived; 2) Designing for autonomy is an ethical imperative critical to the future design of responsible AI; and 3) Autonomy-support must be analysed from at least six spheres of experience in order to approriately capture contradictory and downstream effects.

Vehicle externalism maintains that the vehicles of our mental representations can be located outside of the head, that is, they need not be instantiated by neurons located inside the brain of the cogniser. But some disagree, insisting that ‘non-derived’, or ‘original’, content is the mark of the cognitive and that only biologically instantiated representational vehicles can have non-derived content, while the contents of all extra-neural representational vehicles are derived and thus lie outside the scope of the cognitive. In this paper we develop one aspect of Menary’s vehicle externalist theory of cognitive integration—the process of enculturation—to respond to this longstanding objection. We offer examples of how expert mathematicians introduce new symbols to represent new mathematical possibilities that are not yet understood, and we argue that these new symbols have genuine non-derived content, that is, content that is not dependent on an act of interpretation by a cognitive agent and that does not derive from conventional associations, as many linguistic representations do.

The extended mind thesis prompted philosophers to think about the different shapes our minds can take as they reach beyond our brains and stretch into new technologies. Some of us rely heavily on the environment to scaffold our cognition, reorganizing our homes into rich cognitive niches, for example, or using our smartphones as swiss-army knives for cognition. But the thesis also prompts us to think about other varieties of minds and the unique forms they take. What are we to make of the exotic distributed nervous systems we see in octopuses, for example, or the complex collectives of bees? In this paper, I will argue for a robust version of the extended mind thesis that includes the possibility of extended consciousness. This thesis will open up new ways of understanding the different forms that conscious minds can take, whether human or nonhuman. The thesis will also challenge the popular belief that consciousness exists exclusively in the brain. Furthermore, despite the attention that the extended mind thesis has received, there has been relatively less written about the possibility of extended consciousness. A number of prominent defenders of the extended mind thesis have even called the idea of extended consciousness implausible. I will argue, however, that extended consciousness is a viable theory and it follows from the same ‘parity argument’ that Clark and Chalmers first advanced to support the extended mind thesis. What is more, it may even provide us with a valuable paradigm for how we understand some otherwise puzzling behaviors in certain neurologically abnormal patients as well as in some nonhuman animals.

Alan Turing, one of the fathers of computing, warned that Artificial Intelligence (AI) could one day pose an existential risk to humanity. Today, recent advancements in the field AI have been accompanied by a renewed set of existential warnings. But what exactly constitutes an existential risk? And how exactly does AI pose such a threat? In this chapter we aim to answer these questions. In particular, we will critically explore three commonly cited reasons for thinking that AI poses an existential threat to humanity: the control problem, the possibility of global disruption from an AI race dynamic, and the weaponization of AI.