This paper presents an overview of the risks and benefits of Social AI, understood as conversational AI systems that cater to human social needs like romance, companionship, or entertainment. Section 1 of the paper provides a brief history of conversational AI systems and introduces conceptual distinctions to help distinguish varieties of Social AI and pathways to their deployment. Section 2 of the paper adds further context via a brief discussion of anthropomorphism and its relevance to assessment of human-chatbot relationships. Section (...) 3 of the paper provides a survey of potential and in some cases demonstrated harms associated with user interactions with Social AI systems. Finally, Section 4 discusses how the benefits and harms of Social AI can best be addressed, with a primary focus on how frameworks from AI ethics can inform their development. (shrink)

Does the assumption of a weak form of computational functionalism, according to which the right form of neural computation is sufficient for consciousness, entail that a digital computational simulation of such neural computations is conscious? Or must this computational simulation be implemented in the right way, in order to replicate consciousness? From the perspective of Karl Friston’s free energy principle, self-organising systems (such as living organisms) share a set of properties that could be realised in artificial systems, but are not (...) instantiated by computers with a classical (von Neumann) architecture. I argue that at least one of these properties, viz. a certain kind of causal flow, can be used to draw a distinction between systems that merely simulate, and those that actually replicate consciousness. (shrink)

In light of recent breakneck pace in machine learning, questions about whether near-future artificial systems might be conscious and possess moral status are increasingly pressing. This paper argues that as matters stand these debates lack any clear criteria for resolution via the science of consciousness. Instead, insofar as they are settled at all, it is likely to be via shifts in public attitudes brought about by the increasingly close relationships between humans and AI users. Section 1 of the paper I (...) briefly lays out the current state of the science of consciousness and its limitations insofar as these pertain to machine consciousness, and claims that there are no obvious consensus frameworks to inform public opinion on AI consciousness. Section 2 examines the rise of conversational chatbots or Social AI, and argues that in many cases, these elicit strong and sincere attributions of consciousness, mentality, and moral status from users, a trend likely to become more widespread. Section 3 presents an inconsistent triad for theories that attempt to link consciousness, behaviour, and moral status, noting that the trends in Social AI systems will likely make the inconsistency of these three premises more pressing. Finally, Section 4 presents some limited suggestions for how consciousness and AI research communities should respond to the gap between expert opinion and folk judgment. (shrink)

Does the capacity to think require the capacity to sense? A lively debate on this topic runs throughout the history of philosophy and now animates discussions of artificial intelligence. Many have argued that AI systems such as large language models cannot think and understand if they lack sensory grounding. I argue that thought does not require sensory grounding: there can be pure thinkers who can think without any sensory capacities. As a result, the absence of sensory grounding does not entail (...) that large language models cannot think or understand. I also consider to what extent quasi-sensory grounding can at least boost the performance of a language model. (shrink)

Griefbots are chatbots modeled on the personalities of deceased individuals, designed to assist with the grieving process and, according to some, to continue relationships with loved ones after their physical passing. The essay examines the promises and perils of griefbots from a Daoist perspective. According to the Daoist philosopher Zhuangzi, death is a natural and inevitable phenomenon, a manifestation of the constant changes and transformations in the world. This approach emphasizes adaptability, flexibility, and openness to alternative ways of relating to (...) the deceased after they are gone. Drawing on the Daoist ideas, this essay distinguishes between two importantly different ways to continue a relationship with the deceased, and correspondingly, two kinds of griefbot designs: preservational griefbots and transformational griefbots. A preservational griefbot, which aims to preserve the user’s relationship with the deceased, presents itself as the deceased individual by replicating the deceased’s personality as closely and for as long as possible. By contrast, a transformational griefbot aligns more with Zhuangist ideals and aims to facilitate the transformation of our relationship with the deceased. It helps the bereaved come to terms with their loss and move on with their lives, without abandoning their relationship with the deceased. Specifically, it facilitates this transformation in two ways: first, by helping the bereaved release themselves from their habitual ways of relating to the loved one, thereby bringing closure to their relationship in its previous form; and second, by enabling the bereaved to continue interacting with the ideas and legacies of the deceased in a new and digitally mediated way, which, in turn, reshapes and enriches the content and significance of their relationship. (shrink)

This chapter considers the potential of artificial intelligence (AI) to exhibit creativity and imagination, in light of recent advances in generative AI and the use of deep neural networks (DNNs). Reasons for doubting that AI exhibits genuine creativity or imagination are considered, including the claim that the creativity of an algorithm lies in its developer, that generative AI merely reproduces patterns in its training data, and that AI is lacking in a necessary feature for creativity or imagination, such as consciousness, (...) agency, metacognitive awareness, or the use of mental imagery. In each case, a counterargument is provided that AI already has or soon will overcome the apparent barrier. In some instances, this is because the critical features deemed lacking in AI may not be necessary for human creativity and imagination either. In others, it is because important developmental and structural similarities between humans and generative AI have been insufficiently appreciated. (shrink)

In this paper, I examine why moral exemplars are useful and even necessary in moral education despite several critiques from researchers and educators. To support my point, I review recent AI research demonstrating that exemplar-based learning is superior to rule-based learning in model performance in training neural networks, such as large language models. I particularly focus on why education aiming at promoting the development of multifaceted moral functioning can be done effectively by using exemplars, which is similar to exemplar-based learning (...) in AI model training. Furthermore, I discuss the potential limitations and issues related to exemplar-applied moral education with findings from recent studies in AI research raising concerns about model biases and toxic outcomes. I attempt to propose ways to address the concerns regarding employing moral exemplars as well. As remedies, I suggest that autonomy-supporting deliberative and reflective learning processes should be utilized. Furthermore, based on the discussion, I examine how macroscopic socio-cultural aspects influence the effectiveness of exemplar-applied moral education. Suggestions for moral educators and future directions for research in moral education are briefly discussed. (shrink)

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users anthropomorphise them. (...) For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. (shrink)

There has been a surge of interest in explainable artificial intelligence (XAI). It is commonly claimed that explainability is necessary for trust in AI, and that this is why we need it. In this paper, I argue that for some notions of trust it is plausible that explainability is indeed a necessary condition. But that these kinds of trust are not appropriate for AI. For notions of trust that are appropriate for AI, explainability is not a necessary condition. I thus (...) conclude that explainability is not necessary for trust in AI that matters. (shrink)

Most philosophers think that phenomenal consciousness underlies, or at any rate makes a large contribution, to moral considerability. This paper argues that many such accounts invoke question-begging arguments. Moreover, they’re unable to explain apparent differences in moral status across and within different species. In the light of these problems, I argue that we ought to take very seriously a view according to which moral considerability is grounded in functional properties. Phenomenal consciousness may be sufficient for having a moral value, but (...) it may not be necessary, and it may contribute relatively little to our overall considerability. (shrink)

While there has been much discussion of whether AI systems could function as moral agents or acquire sentience, there has been very little discussion of whether AI systems could have free will. I sketch a framework for thinking about this question, inspired by Daniel Dennett’s work. 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 we have good explanatory reasons 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)

One relatively neglected challenge in ethical artificial intelligence (AI) design is ensuring that AI systems invite a degree of emotional and moral concern appropriate to their moral standing. Although experts generally agree that current AI chatbots are not sentient to any meaningful degree, these systems can already provoke substantial attachment and sometimes intense emotional responses in users. Furthermore, rapid advances in AI technology could soon create AIs of plausibly debatable sentience and moral standing, at least by some relevant definitions. Morally (...) confusing AI systems create unfortunate ethical dilemmas for the owners and users of those systems, since it is unclear how those systems ethically should be treated. I argue here that, to the extent possible, we should avoid creating AI systems whose sentience or moral standing is unclear and that AI systems should be designed so as to invite appropriate emotional responses in ordinary users. (shrink)

1. 1.1. Suffering is bad. This is why, ceteris paribus, there are strong moral reasons to prevent suffering. Moreover, typically, those moral reasons are stronger when the amount of suffering at st...

Many different methodological approaches have been proposed to infer the presence of consciousness in non-human systems. In this paper, a version of the theory-heavy approach is defended. Theory-heavy approaches rely heavily on considerations from theories of consciousness to make inferences about non-human consciousness. Recently, the theory-heavy approach has been critiqued in the form of Birch's (Noûs, 56(1): 133-153, 2022) dilemma of demandingness and Shevlin's (Mind & Language, 36(2): 297-314, 2021) specificity problem. However, both challenges implicitly assume an inapt characterization of (...) the theory-heavy approach. I argue that an alternative characterization of the approach, what I call a credence-based theory-heavy approach, avoids these challenges. Theorists can generate interpretations of their theory, at different levels of generality, and operationalize these into theory-informed markers. These theory-informed are assigned a likelihood and are used to assess the probability that a target system is conscious. In providing this characterization, and mapping out the possible ways in which a credence-based theory-heavy approach can be fleshed out, the aim is to situate the theory-heavy approach as a more compelling approach than it is currently being perceived as. Our attention, then, needs to towards remaining challenges such as the consensus problem and the problem of calibrating the likelihoods associated with theory-informed markers. I also explore methodological pluralism and assess how the credence-based theory-heavy approach can benefit from other methodological approaches. (shrink)

Imagine that you are given access to an AI chatbot that compellingly mimics the personality and speech of a deceased loved one. If you start having regular interactions with this “thanabot,” could this new relationship be a continuation of the relationship you had with your loved one? And could a relationship with a thanabot preserve or replicate the value of a close human relationship? To the first question, we argue that a relationship with a thanabot cannot be a true continuation (...) of your relationship with a deceased loved one, though it might support one’s continuing bonds with the dead. To the second question, we argue that, in and of themselves, relationships with thanabots cannot benefit us as much as rewarding and healthy intimate relationships with other humans, though we explain why it is difficult to make reliable comparative generalizations about the instrumental value of these relationships. (shrink)

I develop the anticipatory argument for the view that it is nomologically possible that some non-biological creatures are phenomenally conscious, including conventional, silicon-based AI systems. This argument rests on the general idea that we should make our beliefs conform to the outcomes of an ideal scientific process and that such an ideal scientific process would attribute consciousness to some possible AI systems. This kind of ideal scientific process is an ideal application of the iterative natural kind (INK) strategy, according to (...) which one should investigate consciousness by treating it as a natural kind which iteratively explains observable patterns and correlations between potentially consciousness-relevant features. The relevant AI systems are psychological duplicates. These are hypothetical non-biological creatures which share the coarse-grained functional organization of humans. I argue that an ideal application of the INK strategy would attribute consciousness to psychological duplicates because this gives rise to a simpler and more unifying explanatory account of biological and non-biological cognition. If my argument is sound, then creatures made from the same material as conventional AI systems can be conscious, thus removing one of the main uncertainties for assessing AI consciousness and suggesting that AI consciousness may be a serious near-term concern. (shrink)

The arrival of image-making generative artificial intelligence (AI) programs has been met with a broad rebuke: to many, it feels inherently wrong to regard images made using generative AI programs as artworks. I am skeptical of this sentiment, and in what follows I aim to demonstrate why. I suspect AI generated images can be considered artworks; more specifically, that generative AI programs are, in many cases, just another tool artists can use to realize their creative intent. I begin with an (...) overview of how generative AI programs, like OpenAI’s DALL-E 2, work. Then, leveraging work by Claire Anscomb, I argue that generative AI programs are a new technique of automatic image-making that affords creative agency to its users, thereby qualifying the images they create as artworks. Finally, I show many of the objections brought against AI artworks—including accusations of plagiarism and artistic devaluation—are due to the social backdrop in which we currently find them, rather than the technology itself. In the end, I aim to open the door to further aesthetic debate concerning AI generated images and art. (shrink)

ABSTRACT Imagine that you are given access to an AI chatbot that compellingly mimics the personality and speech of a deceased loved one. If you start having regular interactions with this “thanabot,” could this new relationship be a continuation of the relationship you had with your loved one? And could a relationship with a thanabot preserve or replicate the value of a close human relationship? To the first question, we argue that a relationship with a thanabot cannot be a true (...) continuation of your relationship with a deceased loved one, though it might support one’s continuing bonds with the dead. To the second question, we argue that, in and of themselves, relationships with thanabots cannot benefit us as much as rewarding and healthy intimate relationships with other humans, though we explain why it is difficult to make reliable comparative generalizations about the instrumental value of these relationships. (shrink)

Chatbots powered by large language models (LLMs) are increasingly capable of engaging in what seems like natural conversations with humans. This raises the question of whether we should interact with these chatbots in a morally considerate manner. In this chapter, we examine how to answer this question from within the normative framework of virtue ethics. In the literature, two kinds of virtue ethics arguments, the moral cultivation and the moral character argument, have been advanced to argue that we should afford (...) moral treatment to social robots. However, we propose a moral character argument against the view that we should afford moral treatment to LLM-based chatbots drawing on the notion of practical wisdom. Practical wisdom in this context consists in the skill to discern genuine and artificial sociality. Drawing on ideas from phenomenological philosophy, we argue that this involves the ability to distance oneself from direct social perception and the ability to widen one’s awareness over one’s apparently social interactions. We conclude by suggesting that this skill is a kind of technomoral wisdom required to live well with advanced, social AI systems. (shrink)

I show that there are good reasons to think that some individuals without any capacity for consciousness should be counted as welfare subjects, assuming that desire-fulfilment is a welfare good and that any individuals who can accrue welfare goods are welfare subjects. While other philosophers have argued for similar conclusions, I show that they have done so by relying on a simplistic understanding of the desire-fulfilment theory. My argument is intended to be sensitive to the complexities and nuances of contemporary (...) developments of the theory, while avoiding highly counter-intuitive implications of previous arguments for the same conclusion. (shrink)

In this paper, we argue that Midjourney—a generative AI program that transforms text prompts into images—should be understood not as an agent or a tool, but as a new type of artistic medium. We first examine the view of Midjourney as an agent, considering whether it could be seen as an artist or co-author. This perspective proves unsatisfactory, as Midjourney lacks intentionality and mental states. We then explore the notion of Midjourney as a tool, highlighting its unpredictability and the limited (...) fine-grained control users have over its outputs, which sets it apart from traditional artistic tools. We propose that creating images with Midjourney parallels a gardener's interaction with natura naturans—nature's autonomous generative processes. Similarly, Midjourney acts as machina naturans, where users engage with its dynamic recalcitrance to influence, but not fully control, the creative process. This approach presents Midjourney as an artistic medium shaped by its inherent unpredictability. (shrink)

Edmund Husserl, the founder of phenomenological philosophy, developed the concept of the so-called pure transcendental consciousness. The author of the article asks whether the concept of consciousness understood this way can constitute a model for AI consciousness. It should be remembered that transcendental consciousness is the result of the use of the phenomenological method, the essence of which is referring to experience (“back to things themselves”). Therefore, one can legitimately ask whether the consciousness that AI can achieve can possess the (...) characteristics attributed by Husserl to pure transcendental consciousness. The answer to such questions seems to be negative because AI, as created by humans, can only operate in the field of phenomena. Human intelligence, however, is capable of operating at the ontological level. In the face of difficulties in understanding the phenomenon of consciousness on a scientific basis, the question arises about the possibility of using the phenomenological concept of consciousness developed by Husserl as a starting point in analyzes aimed at answering the question about AI consciousness (The aim of the article is not to discuss in detail the contemporary state of research on consciousness. Therefore, many results currently achieved mainly in the field of neuroscience are omitted. It is just about indicating the possible application of Husserl’s transcendental concept of consciousness in research on AI consciousness). (shrink)

Large language models now possess human-level linguistic abilities in many contexts. This raises the concern that they can be used to deceive and manipulate on unprecedented scales, for instance spreading political misinformation on social media. In future, agentic AI systems might also deceive and manipulate humans for their own purposes. In this paper, first, I argue that AI-generated content should be subject to stricter standards against deception and manipulation than we ordinarily apply to humans. Second, I offer new characterizations of (...) AI deception and manipulation meant to support such standards, according to which a statement is deceptive (resp. manipulative) if it leads human addressees away from the beliefs (resp. choices) they would endorse under “semi-ideal” conditions. Third, I propose two measures to guard against AI deception and manipulation, inspired by this characterization: “extreme transparency” requirements for AI-generated content and “defensive systems” that, among other things, annotate AI-generated statements with contextualizing information. Finally, I consider to what extent these measures can protect against deceptive behavior in future, agentic AI systems. (shrink)

Rapid advancements in large language models (LLMs) have renewed interest in the question of whether consciousness can arise in an artificial system, like a digital computer. The general consensus is that LLMs are not conscious. This paper evaluates the main arguments against artificial consciousness in LLMs and argues that none of them show what they intend. However strong our intuitions against artificial consciousness are, they currently lack rational support.

While AI systems are increasingly assuming roles traditionally occupied by human epistemic authorities (EAs), their epistemological status remains unclear. This paper aims to address this lacuna by assessing the potential for AI systems to be recognized as artificial epistemic authorities. In a first step, I examine the arguments against considering AI systems as EAs, in particular the established model of EAs as engaging in intentional belief transfer via testimony to laypeople – a process seemingly inapplicable to intentionless and beliefless AI. (...) Despite this, AI systems exhibit striking epistemic parallels with human EAs, including epistemic asymmetry and opacity that give rise to comparable challenges for both laypeople and AI users. These challenges include the identification problem – how to recognize reliable EAs/AI systems – and the deference problem – determining the appropriate epistemic stance towards EAs/AI systems. Faced with this dilemma, I discuss three possible solutions: 1. reject the concept of artificial EAs, 2. accept that AI can possess beliefs and intentions and thus align with the standard model, or 3. develop an alternative model that encompasses artificial EAs. I argue that while each option has its benefits and costs, a particularly strong case can be made for option 3. (shrink)

Emotive artificial intelligences are physically or virtually embodied entities whose behavior is driven by artificial intelligence, and which use expressions usually associated with emotion to enhance communication. These entities are sometimes thought to be deceptive, insofar as their emotive expressions are not connected to genuine underlying emotions. In this paper, I argue that such entities are indeed deceptive, at least given a sufficiently broad construal of deception. But, while philosophers and other commentators have drawn attention to the deceptive threat of (...) emotive artificial intelligences, I argue that such entities also pose an overlooked skeptical threat. In short, the widespread existence of emotive signals disconnected from underlying emotions threatens to encourage skepticism of such signals more generally, including emotive signals used by human persons. Thus, while designing artificially intelligent entities to use emotive signals is thought to facilitate human-AI interaction, this practice runs the risk of compromising human-human interaction. (shrink)

Linguistic competence is, among other things, the cognitive ability of using words appropriately. Following this notion, being a competent language user cannot be merely simulated because pretending to use words appropriately already is using words appropriately. Since current AI already can use words appropriately it may be that AI already has linguistic competence instead of merely simulating it. However, in this article I argue that there is at least one domain of linguistic competence that can be simulated: grasping lexical effects. (...) Since grasping lexical effects presupposes an ability which AI lacks, namely feeling emotions, I argue that AI can only grasp the cognitive part of words but not their lexical effects. AI can only simulate to grasp lexical effects. Hence, lexical effects are a domain of linguistic competence AI cannot grasp. (shrink)

A central question in AI ethics concerns the moral status of robots. This article argues against the idea that they have moral status. It proceeds by defending the assumption that consciousness is necessary for welfare subjectivity. Since robots most likely lack consciousness, and welfare subjectivity is necessary for moral status, it follows that robots lack moral status. The assumption that consciousness is necessary for welfare subjectivity appears to be in tension with certain widely accepted theories of wellbeing, especially versions of (...) Desire Satisfaction Theory and Objective List Theory. However, instead of elevating non-conscious robots to welfare subjects, this tension should lead us to reject versions of these theories that have this implausible implication. (shrink)

The ontological presupposition of artificial intelligence (AI) is the liberal autonomous human subject of Locke and Kant, and the ideology of AI is the automation of this particular conception of intelligence. This is demonstrated in detail in classical AI by the work of Simon, who explicitly connected his work on AI to a wider programme in cognitive science, economics, and politics to perfect capitalism. Although Dreyfus produced a powerful Heideggerian critique of classical AI, work on neural networks in AI was (...) ultimately based on the individual as the locus of intelligence. Yet this conception of AI both fails to grasp the essence of large language models, which are a statistical model of human language on the Web. The training data that enables AI is the surveillance and capture of data, where the data creates a model to approximate the entire world. However, there is a more hidden ideology inherent in AI where the goal is not to perfect a model but to control the world. As prompted by an argument between Mead and Bateson, social change is prevented by the application of cybernetics to society as a whole. The goal of AI is not just to replace human beings, but to manage humans to preserve existing power relations. As the source of intelligence in AI is distributed cognition between humans and machines, the alternative to AI is collective intelligence. As theorized by Licklider and Engelbart at the dawn of the Internet, collective intelligence explains how computers weave together both human and non-human intelligence. Rather than replace human intelligence, this produces ever more complex collective forms of intelligence. Rather than meta-stabilize a society of control, collective intelligence can go outside individualist capitalist ontology by incorporating the open world of the pluriverse, as theorized by Escobar. Collective intelligence then stands as an alternative ontological path for AI which puts intelligence at the service of humanity and the world rather than a technocratic elite. (shrink)

This paper argues that individual propositional knowledge, as traditionally analyzed in terms of true-justified-ungettiered belief, does not require phenomenal experience. Accordingly, those who are satisfied with the traditional conception need to come to terms with the possibility that AI and other zombies that lack phenomenal experience possess knowledge. Alternatively, those who resist attributing knowledge to AI based on the assumption that knowledge requires phenomenal experience need to modify or replace the traditional conception of knowledge to incorporate this requirement.

Large language models (LLMs) produce sequences learned as statistical patterns from large corpora. Their emergent status as representatives of the advances in artificial intelligence (AI) have led to an increased attention to the possibilities of regulating the automated production of linguistic utterances and interactions with human users in a process that computer scientists refer to as ‘alignment’—a series of technological and political mechanisms to impose a normative model of morality on algorithms and networks behind the model. Alignment, which can be (...) viewed as the superimposition of normative structure onto a statistical model, however, reveals a conflicted and complex history of the conceptualisation of an interrelationship between language, mind and technology. This relationship is shaped by and, in turn, influences theories of language, linguistic practice and subjectivity, which are especially relevant to the current sophistication in artificially produced text. In this paper, we propose a critical evaluation of the concept of alignment, arguing that the theories and practice behind LLMs reveal a more complex social and technological dynamic of output coordination. We examine this dynamic as a two-way interaction between users and models by analysing how ChatGPT4 redacts perceived ‘anomalous’ language in fragments of Joyce’s Ulysses. We then situate this alignment problem historically, revisiting earlier postwar linguistic debates which counterposed two views of meaning: as discrete structures, and as continuous probability distributions. We discuss the largely occluded work of the Moscow Linguistic School, which sought to reconcile this opposition. Our attention to the Moscow School and later related arguments by Searle and Kristeva casts the problem of alignment in a new light: as one involving attention to the social regulation of linguistic practice, including rectification of anomalies that, like the Joycean text, exist in defiance of expressive conventions. The “problem of alignment” that we address here is, therefore, twofold: on one hand, it points to its narrow and normative definition in current technological development and critical research and, on the other hand, to the reality of complex and contradictory relations between subjectivity, technology and language that alignment problems reveal. (shrink)

In a recent article, Joshua Shepherd identifies six problems with attributing moral status to nonhumans on the basis of consciousness (Shepherd 2023). In this commentary, I want to draw out yet ano...

It seems plausible that digital doppelgängers may help us achieve certain aims or ostensible goods of person-span extension (Iglesias et al. 2025). I am thinking of the writer example who only need...

The scientific study of consciousness, also referred to as consciousness science, is a young scientific field devoted to understanding how conscious experiences and the brain relate. It comprises a host of theories, experiments, and analyses that aim to investigate the problem of consciousness empirically, theoretically, and conceptually. This thesis addresses some of the questions that arise in these investigations from a formal and mathematical perspective. These questions concern theories of consciousness, experimental paradigms, methodology, and artificial consciousness. -/- Regarding theories of (...) consciousness, the thesis contributes to the understanding of the mathematical structure that some of the formal theories in the field propose. The work presented here targets the theory of consciousness known as Integrated Information Theory (IIT) and the neuroscientific theory known as Predictive Processing Theory or Free Energy Principle in its Active Inference form (AI-PP). The thesis provides axiomatic definitions of the mathematical structures that constitute these theories, and uses these definitions to address some of the open questions surrounding the theories. For AI-PP, this includes a rigorous derivation of the formula for Active Inference via Free Energy minimisation and a proof of compositionality of Free Energy. For IIT, this includes resolutions of some of the criticisms of IIT's formal scope and applications, but also the identification of new issues that concern the formalism and its derivation. When possible, the definitions are provided in the mathematical framework of category theory. -/- Regarding experiments, the thesis addresses the main paradigm for testing and falsifying theories of consciousness currently applied in the field. This paradigm consists of comparing the conscious experience that a theory predicts with the conscious experience that is inferred from behavioural data or report by use of measures of consciousness. The thesis provides a formal model of this paradigm and shows that under a certain condition—if inference and prediction are independent—, any minimally informative theory of consciousness can always be falsified. This is deeply problematic since the field’s reliance on report or behaviour to infer conscious experiences, in conjunction with the general structure of most contemporary theories of consciousness, implies such independence. This observation provides the exact formal underpinning of the well-known unfolding argument. The thesis analyses the origin of the problem and identifies precisely which changes are required to avoid this problem in future research. The thesis furthermore shows that the problem of falsifying theories of consciousness, and of empirical comparisons of theories of consciousness more generally, follows from a pervasive closure paradigm in consciousness science, that consists of taking a neuroscientific account of the brain as input to a theory of consciousness, so as to explain what consciousness is, without allowing for modifications or adaptations of the neuroscientific account that would accommodate consciousness as part of the brain's functioning. As is shown in the thesis, this paradigm has implications that point to a fundamental need of revision. -/- Regarding methodological and conceptual questions, the thesis contributes to the foundations of structural research in consciousness science. Structural research aims to use mathematical structures or mathematical spaces, instead of verbal descriptions or simple categorisations, to represent conscious experiences scientifically, for example when building theories of consciousness, or when exploring new empirical avenues to measure consciousness. Despite considerable advances in this realm, there was, prior to this thesis, no explicit definition of what a mathematical structure of conscious experience should be; that is, how the attribution of mathematical structure to conscious experiences should be systematically understood. Perhaps the most important contribution of this thesis to the field is to propose such a definition. The definition, a structural concept, extends existing approaches wherever available, and provides a basis for developing a common formal language to study consciousness, bridging developments as far apart as psychophysics and phenomenology. In addition, and independently of this proposal, the thesis offers a critical analysis of which metaphysical premises need to be presumed in structural research, whether the use of particular formal tools (such as structure-preserving mappings or homomorphisms) is justified, and how structural theories of consciousness could otherwise be built in the first place. An attempt to expand the results from consciousness to more general problems in philosophy of science is made in the context of the well-known Newman problem. -/- Regarding the question of artificial consciousness—can AI feel?—, the thesis contributes two results that take the form of no-go theorems, as known from physics. The first no-go theorem shows that if consciousness is relevant for the temporal evolution of a system's states—if it is dynamically relevant—, then contemporary AI systems cannot be conscious. That is because AI systems run on CPUs, GPUs, TPUs or other processors which have been designed and verified to adhere to computational dynamics that systematically preclude or suppress deviations. The second no-go theorem is situated in the context of computational functionalism, a view which posits that consciousness is a computation. The theorem shows that if computational functionalism holds true, consciousness cannot be a Turing computation. Rather, it must be a novel type of computation that has recently been proposed by Geoffrey Hinton, called mortal computation. -/- This thesis is part of a global effort to pioneer a mathematical perspective in consciousness science, now called Mathematical Consciousness Science. The hope behind the research carried out in this PhD is to illustrate the power and usefulness of mathematical approaches in different areas of consciousness science, and in doing so, to lay the foundations for future mathematical work that complements and supports empirical and theoretical work in the further development of this exciting field. (shrink)

Religion-based AI chatbots serve religious practitioners by bringing them godly wisdom through technology. These bots reply to spiritual and worldly questions by drawing insights or citing verses from the Quran, the Bible, the Bhagavad Gita, the Torah, or other holy books. They answer religious and theological queries by claiming to offer historical contexts and providing guidance and counseling to their users. A criticism of these bots is that they may give inaccurate answers and proliferate bias by propagating homogenized versions of (...) the religions they represent. These “embodied spiritual machines” may likewise bear bias against women, their gender, and their societal roles. This paper crafts a concept intended to address this GPT issue by reimagining, modifying, and implementing a feminist approach to these chatbots. It examines the concepts and designs of these bots and how they address women-related questions. Along with the challenge of bringing gender and diversity-sensitive religious wisdom closer to the people through technology, the paper proposes a re-engineered model of a fair religion-based AI chatbot. (shrink)

Review of Christof Koch, "States of mind: a neuroscientist searches for the seat of consciousness.".

One of the most challenging positions in contemporary philosophy of animal consciousness is that proposed by Peter Carruthers (2018a, 2018b, 2019, 2020). According to Carruthers, there is no fact of the matter about whether animals instantiate conscious states. This radical conclusion arises from the conjunction of two theses he endorses: the global workspace theory and the phenomenal concept strategy. This paper argues against Carruthers’ radical viewpoint. Its structure is as follows. First, I will present Carruthers’ theses on consciousness, such as (...) the all-or-nothing characterization of consciousness and the distinction between ‘qualia realism’ and ‘qualia irrealism’. Subsequently, I will provide a brief overview of the global workspace theory and the phenomenal concept strategy. Next, I will reconstruct the arguments that underpin Carruthers’ skepticism about attributing consciousness to animals. Finally, I will present two arguments that challenge Carruthers’ position, highlighting inherent contradictions within his project. Contrary to Carruthers’ controversial assertion (2020, p. 18), I will conclude that animal consciousness deserves attention from both philosophy and the cognitive sciences. (shrink)