On June 11th, 2022, Blake Lemoine, an engineer at Google, went public with his concerns about the possible sentience of a natural language generation program, LaMDA, that he was testing. In this paper I will defend Blake Lemoine and argue that he was correct in raising the issue of the possible sentience of LaMDA. We will first briefly discuss the specifics of the case and then delve into the science behind LaMDA. Several tests of machine sentience (...) will be reviewed and it will be argued that LaMDA would likely perform well on many of them. Next, we will discuss the question of sentience, both from a scientific and philosophical perspective. Then we will examine the two major approaches in the scientific study of consciousness and ask what they have to say about sentience. We will also examine the relationship between sentience, intelligence, understanding and causality. Finally, using what we have learned we will argue that there is indeed validity to Lemoine’s claim that LaMDA is sentient. (shrink)

The aim of this thesis is to advance a philosophically justifiable account of Artificial Moral Agency (AMA). Concerns about the moral status of Artificial Intelligence (AI) traditionally turn on questions of whether these systems are deserving of moral concern (i.e. if they are moral patients) or whether they can be sources of moral action (i.e. if they are moral agents). On the Organic View of Ethical Status, being a moral patient is a necessary condition for an entity to qualify as (...) a moral agent. This view claims that because artificial agents (AAs) lack sentience, they cannot be proper subjects of moral concern and hence cannot be considered to be moral agents. I raise conceptual and epistemic issues with regards to the sense of sentience employed on this view, and I argue that the Organic View does not succeed in showing that machines cannot be moral patients. Nevertheless, irrespective of this failure, I also argue that the entire project is misdirected in that moral patiency need not be a necessary condition for moral agency. Moreover, I claim that whereas machines may conceivably be moral patients in the future, there is a strong case to be made that they are (or will very soon be) moral agents. Whereas it is often argued that machines cannot be agents simpliciter, let alone moral agents, I claim that this argument is predicated on a conception of agency that makes unwarranted metaphysical assumptions even in the case of human agents. Once I have established the shortcomings of this “standard account”, I move to elaborate on other, more plausible, conceptions of agency, on which some machines clearly qualify as agents. Nevertheless, the argument is still often made that while some machines may be agents, they cannot be moral agents, given their ostensible lack of the requisite phenomenal states. Against this thesis, I argue that the requirement of internal states for moral agency is philosophically unsound, as it runs up against the problem of other minds. In place of such intentional accounts of moral agency, I provide a functionalist alternative, which makes conceptual room for the existence of AMAs. The implications of this thesis are that at some point in the future we may be faced with situations for which no human being is morally responsible, but a machine may be. Moreover, this responsibility holds, I claim, independently of whether the agent in question is “punishable” or not. (shrink)

In early modern times it was not uncommon for thinkers to tease out from the nature of God various doctrines of substantial physical and metaphysical import. This approach was particularly fruitful in the so-called beast-machine controversy, which erupted following Descartes’ claim that animals are automata, that is, pure machines, without a spiritual, incorporeal soul. Over the course of this controversy, thinkers on both sides attempted to draw out important truths about the status of animals simply from the notion or (...) attributes of God. Automatists – led by Nicolas Malebranche and Antoine Dilly – developed six such arguments, appealing to divine justice, providence, economy, glory (twice) and wisdom, while opponents to animal automatism developed two arguments, appealing to divine wisdom and goodness. In this article I shall examine the substance of all eight of these arguments, along with their origins, patronage, and variations, and the objections they elicited from opponents, with the aim of determining their suitability for use in contemporary debates about animal sentience and consciousness, and hence their relevance for contemporary philosophers. (shrink)

Radical democratic thinking is becoming intrigued by the material situatedness of its political agents and by the role of nonhuman participants in political interaction. At stake here is the displacement of narrow anthropocentrism that currently guides democratic theory and practice, and its repositioning into what we call ‘the nonhuman condition’. This Critical Exchange explores the nonhuman condition. It asks: What are the implications of decentering the human subject via a new materialist reading of radical democracy? Does this reading dilute political (...) agency? Or should this be seen, on the contrary, as an invitation for new voices and demands to enter into democratic assemblages? How might engagement with the more-than-human disrupt or extend theories of radical democracy? Hans Asenbaum and Amanda Machin discuss the human democratic subject both through radical democratic and new materialist lenses. They suggest that a new materialist decentred subject does not lose agency but further gains political responsibility in radical democracy. Jean-Paul Gagnon highlights the possibility of becoming through loss. In losing our anthropocentric arrogance—our understanding of being other and better than animal—we become more connected and discover our place in human-nonhuman assemblages. Melissa Orlie agrees that something can be gained through renouncing dominant human fantasies and presents a ‘radical democratic naturalism’. By renouncing land violence and acknowledging nature’s subjectivity, we can further nonexploitative radical democratic politics. This is where the notion of ‘tidalectic’ processes introduced by James L. Smith is helpful. Focusing on water and the Indigenous knowledges around rivers, he considers how democracy and community can be reimagined as plural, cyclical and attentive to the nonhuman with which the human is inevitably entangled. Diana Leong offers a different approach. Considering the nonhuman from the perspective of Black Studies demands an awareness to how blackness has long been constituted as non- or partially human. (shrink)

This chapter considers philosophical problems concerning non-human (and sometimes human) animals, including their metaphysical, physical, and moral status, their origin, what makes them alive, their functional organization, and the basis of their sensitive and cognitive capacities. I proceed by assuming what most of Descartes’s followers and interpreters have held: that Descartes proposed that animals lack sentience, feeling, and genuinely cognitive representations of things. (Some scholars interpret Descartes differently, denying that he excluded sentience, feeling, and representation from animals, and (...) I consider the evidence for these interpretations as well.) Given that Descartes denied any sort of soul to animals, his other philosophical commitments entailed that he must explain the vital and sensitive powers of non-human animals through purely material causes. Indeed, he welcomed this task, for he was engaged in the larger project of providing purely mechanistic explanations for all natural phenomena of the material world. Animal bodies form functional unities that are adapted to environmental circumstances. In his new physics, Descartes sought to discover or hypothesize material mechanisms that would explain the physiological and behavioral capacities of animals, including how they maintain themselves by seeking food and drink, reproduce themselves, and modify their behavior to fit current circumstances. Metaphysically, his new perspective raised the problem of accounting for the functional unity of the animal body considered as a purely material construction, devoid of an active, organizing power such as the sensitive soul. Descartes’s project becomes even more challenging if we ask whence come such mechanisms that are capable of performing the functions of living things. Officially, Descartes endorsed the accepted theological orthodoxy, that God designed and created the bodily mechanisms of humans and animals. However, in his natural philosophy he set himself the task of explaining the origin of animals as part of the natural development of the universe out of an original chaotic soup of material particles. Within this naturalistic perspective, he must explain how, through purely material processes, the functionally organized bodies of living things (plants and animals) could be produced from non-living matter. Without a designing creator, how do animal bodies arise that are capable of digesting food, growing, reproducing, and performing the behaviors needed to preserve life and health? (shrink)

In the past, philosophers, scientists, and even the general opinion, had no problem in accepting the existence of consciousness in the same way as the existence of the physical world. After the advent of Newtonian mechanics, science embraced a complete materialistic conception about reality. Scientists started proposing hypotheses like abiogenesis (origin of first life from accumulation of atoms and molecules) and the Big Bang theory (the explosion theory for explaining the origin of universe). How the universe came to be what (...) it is now is a key philosophical question. The hypothesis that it came from Nothing (as proposed by Stephen Hawking, among others), proves to be dissembling, since the quantum vacuum can hardly be considered a void. In modern science, it is generally assumed that matter existed before the universe came to be. Modern science hypothesizes that the manifestation of life on Earth is nothing but a mere increment in the complexity of matter — and hence is an outcome of evolution of matter (chemical evolution) following the Big Bang. After the manifestation of life, modern science believed that chemical evolution transformed itself into biological evolution, which then had caused the entire biodiversity on our planet. The ontological view of the organism as a complex machine presumes life as just a chance occurrence, without any inner purpose. This approach in science leaves no room for the subjective aspect of consciousness in its attempt to know the world as the relationships among forces, atoms, and molecules. On the other hand, the Vedāntic view states that the origin of everything material and nonmaterial is sentient and absolute (unconditioned). Thus, sentient life is primitive and reproductive of itself – omne vivum ex vivo – life comes from life. This is the scientifically verified law of experience. Life is essentially cognitive and conscious. And, consciousness, which is fundamental, manifests itself in the gradational forms of all sentient and insentient nature. In contrast to the idea of objective evolution of bodies, as envisioned by Darwin and followers, Vedānta advocates the idea of subjective evolution of consciousness as the developing principle of the world. In this paper, an attempt has been made to highlight a few relevant developments supporting a sentient view of life in scientific research, which has caused a paradigm shift in our understanding of life and its origin. (shrink)

I show that some of the most initially attractive routes of refuting epistemological solipsism face serious obstacles. I also argue that for creatures like ourselves, solipsism is a genuine form of external world skepticism. I suggest that together these claims suggest the following morals: No proposed solution to external world skepticism can succeed which does not also solve the problem of epistemological solipsism. And, more tentatively: In assessing proposed solutions to external world skepticism, epistemologists should explicitly consider whether those solutions (...) extend to knowledge of other minds. Finally, and also tentatively: epistemological solipsism warrants more philosophical attention than it currently enjoys. (shrink)

Self-consciousness relates to important themes, such as sentience and personhood, and is often the cornerstone of moral theories (Warren, 1997). However, not much attention is given to future moral creatures of the earth: robots. This may be due to the unsettled status of their experience, which is why I have chosen to find the necessary features of self-consciousness in them. Philosophy of mind is also my interest which I have developed since I rejected the idea of souls and could (...) not account for my own phenomenal experiences. Among the current answers to the hard problem of consciousness (Chalmers, 1995) one stood out to me – Daniel Dennett’s multiple drafts model of consciousness presented in “Consciousness Explained” (Dennett, 1991) and his other works. Daniel Dennett is a self-described illusionist (Dennett, 2016) and a functionalist. His position is synonymous to holding self-conscious experiences, such as deliberating over one’s pain or reflecting over one’s actions insufficient to postulate existence of a metaphysical self. He believes instead that experiences are functional realizations which do not require any self, and the feeling of being conscious or self-conscious is actually a ”user-illusion” (Dennett, 1991), a term describing the unjustified belief of being in control of one’s actions. In his views, a selfhood as it is popularly conceived is contradictory. Therefore, he rejects realism about mental events, a position also called naïve realism (Siegel, 2011), on the basis that it is unverifiable and there are insufficient grounds to accept mental events (Dennett, 1988). He adopts a similar scepticism towards other theories of minds, hence nearing eliminative materialism in self-consciousness and related theme of pain (Dennett, 1978). His multiple drafts model of consciousness seeks to ground our beliefs about self-consciousness in the specific architecture of mind, which is particular to every species and kind of being. As such it is a particularly flexible theory that examines experiences of non-humans without falling into binary thinking – believing that a being has as rich inner life as humans do or none. This is why it is a great starting point for establishing necessary conditions for consciousness in robots. In this essay, I will often use words computer, programme, robot interchangeably for the purpose of clarity and take as the working definition “autonomous machines capable of sensing their environment, making decision and performing tasks similar to human actions”. I chose to explore self-consciousness instead of committing to the issue of consciousness, because consciousness can be defined in a weak sense, in which even a thermostat is “conscious” of the temperature, and consciousness in a strong sense, which invokes the internalism and externalism debate that is less connected to the nature of experiences than self-consciousness. My exploration pertains first and foremost to the philosophy of mind, of which I will examine the most relevant positions and decide to commit to functionalism (Block, 1980). Then I will advocate for explaining away of some of phenomena of self-consciousness and believing self-consciousness to be a sort of illusion. From there I will conclude that robots could fall for the same illusion of self-consciousness and therefore become self-conscious. I will argue, that self-consciousness can be established using a modified Turing Test, which would determine if a robot believed in a meaningful narrative about itself (Ricoeur, 1991) and hence be self-conscious. This will lead to answering the question of this essay: what are the features necessary for a self-conscious robot in the light of “Consciousness Explained” by Daniel Dennett? (shrink)

In debates about animal sentience, the precautionary principle is often invoked. The idea is that when the evidence of sentience is inconclusive, we should “give the animal the benefit of the doubt” or “err on the side of caution” in formulating animal protection legislation. Yet there remains confusion as to whether it is appropriate to apply the precautionary principle in this context, and, if so, what “applying the precautionary principle” means in practice regarding the burden of proof for (...) animal sentience. Here I construct a version of the precautionary principle tailored to the question of animal sentience together with a practical framework for implementing it. I explain and defend the key features of this framework, argue that it is well-aligned with current practice in animal welfare science, and consider and reject a number of influential counterarguments to the use of precautionary reasoning in this area. (shrink)

One of the primary concerns in animal research is ensuring the welfare of laboratory animals. Modern views on animal welfare emphasize the role of animal sentience, i.e. the capacity to experience subjective states such as pleasure or suffering, as a central component of welfare. The increasing official recognition of animal sentience has had large effects on laboratory animal research. The Cambridge Declaration on Consciousness (Low et al., University of Cambridge, 2012) marked an official scientific recognition of the presence (...) of sentience in mammals, birds, and cephalopods. Animal sentience has furthermore been recognized in legislation in the European Union, UK, New Zealand and parts of Australia, with discussions underway in other parts of the world to follow suit. In this paper, we analyze this shift towards recognition of sentience in the regulation and practice in the treatment of laboratory animals and its effects on animal welfare and use. (shrink)

‘Sentience’ sometimes refers to the capacity for any type of subjective experience, and sometimes to the capacity to have subjective experiences with a positive or negative valence, such as pain or pleasure. We review recent controversies regarding sentience in fish and invertebrates and consider the deep methodological challenge posed by these cases. We then present two ways of responding to the challenge. In a policy-making context, precautionary thinking can help us treat animals appropriately despite continuing uncertainty about their (...) sentience. In a scientific context, we can draw inspiration from the science of human consciousness to disentangle conscious and unconscious perception (especially vision) in animals. Developing better ways to disentangle conscious and unconscious affect is a key priority for future research. (shrink)

I discuss whether "sentience" (i.e., phenomenal consciousness) comes in degrees.

Timothy Hsiao argues that animals lack moral status because they lack the capacity for the sort of higher-level rationality required for membership in the moral community. Stijn Bruers and László Erdős have already raised a number of objections to this argument, to which Hsiao has replied with some success. But I think a stronger critique can be made. Here I raise further objections to three aspects of Hsiao's view: his conception of the moral community, his idea of root capacities grounded (...) in one's nature, and his explanation of why cruelty is wrong. I also argue that sentience is a more plausible candidate for the morally salient capacity than rationality. (shrink)

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...) they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

Many think that a specific aspect of phenomenal consciousness – valenced or affective experience – is essential to consciousness’s moral significance (valence sentientism). They hold that valenced experience is necessary for well-being, or moral status, or psychological intrinsic value (or all three). Some think that phenomenal consciousness generally is necessary for non-derivative moral significance (broad sentientism). Few think that consciousness is unnecessary for moral significance (non-necessitarianism). In this paper I consider the prospects for these views. I first consider the prospects (...) for valence sentientism in light of Vulcans, beings who are conscious but without affect or valence of any sort. I think Vulcans pressure us to accept broad sentientism. But I argue that a consideration of explanations for broad sentientism opens up possible explanations for non-necessitarianism about the moral significance of consciousness. That is, once one leans away from valence sentientism because of Vulcans, one should feel pressure to accept a view on which consciousness is not necessary for well-being, moral status, or psychological intrinsic value. (shrink)

Animalism is prima facie the most plausible view about what we are; it aligns better with science and common sense, and is metaphysically more parsimonious. Thought experiments involving the brain, however, tend to elicit intuitions contrary to animalism. In this paper, I examine two classical thought experiments from the literature, brain transplant and cerebrum transplant, and a new one, cerebrum regeneration. I argue that they are theoretically possible, but that a scientifically informed account of what would actually happen shows that (...) in none of the cases would the person be separated from the animal. Our intuitions in these cases, when adequately informed by neuroscience, do not conflict with animalism – rather, they suggest a correction of the animalist position: the persisting animal should be at least minimally sentient. Sentience animalism is a new formulation of the animalist account of personal identity that allows us to reconcile facts about our biological persistence conditions with the intuition that human persistence should involve some kind of psychological continuity. (shrink)

Sentience is the capacity to have feelings, such as feelings of pain, pleasure, hunger, thirst, warmth, joy, comfort and excitement. It is not simply the capacity to feel pain, but feelings of pain, distress or harm, broadly understood, have a special significance for animal welfare law. Drawing on over 300 scientific studies, we evaluate the evidence of sentience in two groups of invertebrate animals: the cephalopod molluscs or, for short, cephalopods (including octopods, squid and cuttlefish) and the decapod (...) crustaceans or, for short, decapods (including crabs, lobsters and crayfish). We also evaluate the potential welfare implications of current commercial practices involving these animals. (shrink)

I argue that Sellars's philosophy of perception can be reconciled with recent work in enactive cognitive science. Sellars's critical realism holds that we perceive physical objects with perceptible properties as causally mediated by how these objects affect our sensory receptors. I argue that this theory, while basically right, downplays the role of embodiment in perception: perception essentially involves sensorimotor abilities. I argue that embodied critical realism can resolve the debate between Coates and Noe.

Phenomenal consciousness appears to be particularly normatively significant. For this reason, sentience-based conceptions of ethics are widespread. In the field of animal ethics, knowing which animals are sentient appears to be essential to decide the moral status of these animals. I argue that, given that materialism is true of the mind, phenomenal consciousness is probably not particularly normatively significant. We should face up to this probable insignificance of phenomenal consciousness and move towards an ethic without sentience.

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

In consciousness studies there is a growing tendency to consider experience as (i) fundamentally affective and (ii) deeply interlinked with interoceptive and homeostatic bodily processes. However, this view still needs further development to be part of any rigorous theory of consciousness. To advance in this direction, we ask: (1) is there any affective type that is always present in consciousness?, (2) is it related to interoception and homeostasis?, and (3) what are its properties? Here we analyse and compare Jim Russell's (...) core affect and Thomas Fuchs' concept of vitality, and propose a more encompassing notion that subsumes both: continuous organismic sentience. It provides affirmative answers to questions 1 and 2, and, regarding question 3, a preliminary list of thirteen properties divided into ontological, phenomenological, and functional categories. This is the first of a series of studies that will systematically address different notions of a fundamental, homeostatically-rooted affective type, to achieve a rigorous, unified concept for consciousness science. (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)

The machine conception of the organism (MCO) is one of the most pervasive notions in modern biology. However, it has not yet received much attention by philosophers of biology. The MCO has its origins in Cartesian natural philosophy, and it is based on the metaphorical redescription of the organism as a machine. In this paper I argue that although organisms and machines resemble each other in some basic respects, they are actually very different kinds of systems. I submit (...) that the most significant difference between organisms and machines is that the former are intrinsically purposive whereas the latter are extrinsically purposive. Using this distinction as a starting point, I discuss a wide range of dissimilarities between organisms and machines that collectively lay bare the inadequacy of the MCO as a general theory of living systems. To account for the MCO’s prevalence in biology, I distinguish between its theoretical, heuristic, and rhetorical functions. I explain why the MCO is valuable when it is employed heuristically but not theoretically, and finally I illustrate the serious problems that arise from the rhetorical appeal to the MCO. (shrink)

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to (...) enhance model generalizability. (shrink)

This article asks the question, ``what is reliable machine learning?'' As I intend to answer it, this is a question about epistemic justification. Reliable machine learning gives justification for believing its output. Current approaches to reliability (e.g., transparency) involve showing the inner workings of an algorithm (functions, variables, etc.) and how they render outputs. We then have justification for believing the output because we know how it was computed. Thus, justification is contingent on what can be shown about (...) the algorithm, its properties, and its behavior. In this paper, I defend computational reliabilism (CR). CR is a computationally-inspired off-shoot of process reliabilism that does not require showing the inner workings of an algorithm. CR credits reliability to machine learning by identifying reliability indicators external to the algorithm (validation methods, knowledge-based integration, etc.). Thus, we have justification for believing the output of machine learning when we have identified the appropriate reliability indicators. CR is advanced as a more suitable epistemology for machine learning. The main goal of this article is to lay the groundwork for CR, how it works, and what we can expect as a justificatory framework for reliable machine learning. (shrink)

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

The fact that real-world decisions made by artificial intelligences (AI) are often ethically loaded has led a number of authorities to advocate the development of “moral machines”. I argue that the project of building “ethics” “into” machines presupposes a flawed understanding of the nature of ethics. Drawing on the work of the Australian philosopher, Raimond Gaita, I argue that ethical dilemmas are problems for particular people and not (just) problems for everyone who faces a similar situation. Moreover, the force of (...) an ethical claim depends in part on the life history of the person who is making it. For both these reasons, machines could at best be engineered to provide a shallow simulacrum of ethics, which would have limited utility in confronting the ethical and policy dilemmas associated with AI. (shrink)

Large language models (LLMs) represent the currently most relevant incarnation of artificial intelligence with respect to the future fate of democratic governance. Considering their potential, this paper seeks to answer a pressing question: Could LLMs outperform humans as expert advisors to democratic assemblies? While bearing the promise of enhanced expertise availability and accessibility, they also present challenges of hallucinations, misalignment, or value imposition. Weighing LLMs’ benefits and drawbacks compared to their human counterparts, I argue for their careful integration to augment (...) democracy’s ability to address complex policy issues. The paper posits that time-tested democratic procedures like deliberation and aggregation by voting provide safeguards effective against both human and machine advisor imperfections. Additional protective measures include custom LLM training for the advisory role, boosting representatives’ competencies in query formulation, or implementation of adversarial proceedings in which LLM advisors could debate each other and provide dissenting opinions. These could further mitigate the risks that LLMs present in advisory roles and empower human decision-makers toward increased autonomy and quality of their collective choices. My conceptual exploration offers a roadmap for the co-evolution of AI and democratic institutions, setting the stage for an empirical research agenda to finetune the implementation specifics. (shrink)

This article critically examines one of the most prevalent metaphors in modern biology, namely the machine conception of the organism (MCO). Although the fundamental differences between organisms and machines make the MCO an inadequate metaphor for conceptualizing living systems, many biologists and philosophers continue to draw upon the MCO or tacitly accept it as the standard model of the organism. This paper analyses the specific difficulties that arise when the MCO is invoked in the study of development and evolution. (...) In developmental biology the MCO underlies a logically incoherent model of ontogeny, the genetic program, which serves to legitimate three problematic theses about development: genetic animism, neo-preformationism, and developmental computability. In evolutionary biology the MCO is responsible for grounding unwarranted theoretical appeals to the concept of design as well as to the interpretation of natural selection as an engineer, which promote a distorted understanding of the process and products of evolutionary change. Overall, it is argued that, despite its heuristic value, the MCO today is impeding rather than enabling further progress in our comprehension of living systems. (shrink)

We explore the question of whether machines can infer information about our psychological traits or mental states by observing samples of our behaviour gathered from our online activities. Ongoing technical advances across a range of research communities indicate that machines are now able to access this information, but the extent to which this is possible and the consequent implications have not been well explored. We begin by highlighting the urgency of asking this question, and then explore its conceptual underpinnings, in (...) order to help emphasise the relevant issues. To answer the question, we review a large number of empirical studies, in which samples of behaviour are used to automatically infer a range of psychological constructs, including affect and emotions, aptitudes and skills, attitudes and orientations (e.g. values and sexual orientation), personality, and disorders and conditions (e.g. depression and addiction). We also present a general perspective that can bring these disparate studies together and allow us to think clearly about their philosophical and ethical implications, such as issues related to consent, privacy, and the use of persuasive technologies for controlling human behaviour. (shrink)

According to the most popular theories of intentionality, a family of theories we will refer to as “functional intentionality,” a machine can have genuine intentional states so long as it has functionally characterizable mental states that are causally hooked up to the world in the right way. This paper considers a detailed description of a robot that seems to meet the conditions of functional intentionality, but which falls victim to what I call “the composition problem.” One obvious way to (...) escape the problem (arguably, the only way) is if the robot can be shown to be a moral patient – to deserve a particular moral status. If so, it isn’t clear how functional intentionality could remain plausible (something like “phenomenal intentionality” would be required). Finally, while it would have seemed that a reasonable strategy for establishing the moral status of intelligent machines would be to demonstrate that the machine possessed genuine intentionality, the composition argument suggests that the order of precedence is reversed: The machine must first be shown to possess a particular moral status before it is a candidate for having genuine intentionality. (shrink)

A number of findings in the field of machine learning have given rise to questions about what it means for automated scoring- or decisionmaking systems to be fair. One center of gravity in this discussion is whether such systems ought to satisfy classification parity (which requires parity in accuracy across groups, defined by protected attributes) or calibration (which requires similar predictions to have similar meanings across groups, defined by protected attributes). Central to this discussion are impossibility results, owed to (...) Kleinberg et al. (2016), Chouldechova (2017), and Corbett-Davies et al. (2017), which show that classification parity and calibration are often incompatible. This paper aims to argue that classification parity, calibration, and a newer, interesting measure called counterfactual fairness are unsatisfactory measures of fairness, offer a general diagnosis of the failure of these measures, and sketch an alternative approach to understanding fairness in machine learning. (shrink)

Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets of modern life. Ethical concerns about this widespread practice have given rise to the field of fair machine learning and a number of fairness measures, mathematically precise definitions of fairness that purport to determine whether a given prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in this context to be a placeholder for a variety of normative egalitarian (...) considerations. We explore a few fairness measures to suss out their egalitarian roots and evaluate them, both as formalizations of egalitarian ideas and as assertions of what fairness demands of predictive systems. We pay special attention to a recent and popular fairness measure, counterfactual fairness, which holds that a prediction about an individual is fair if it is the same in the actual world and any counterfactual world where the individual belongs to a different demographic group (cf. Kusner et al. (2018)). (shrink)

Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also (...) discuss how work in embodied and situated cognition could provide a valu- able perspective on future research. (shrink)

This public article presents three arguments for the plausibility of panpsychism: the view that sentience is a fundamental and ubiquitous element of actuality. Thereafter is presented a brief exploration of why panpsychism has been spurned. The article was commissioned by High Existence. -/- – Introduction – 1. The Genetic Argument – 2. The Abstraction Argument – 3. The Inferential Argument – Why Panpsychism is Spurned – End Remarks.

In order to determine whether current (or future) machines have a welfare that we as agents ought to take into account in our moral deliberations, we must determine which capacities give rise to interests and whether current machines have those capacities. After developing an account of moral patiency, I argue that current machines should be treated as mere machines. That is, current machines should be treated as if they lack those capacities that would give rise to psychological interests. Therefore, they (...) are moral patients only if they have non-psychological interests. I then provide an account of what I call teleo interests that constitute the most plausible type of non-psychological interest that a being might have. I then argue that even if current machines have teleo interests, they are such that agents need not concern themselves with these interests. Therefore, for all intents and purposes, current machines are not moral patients. (shrink)

In this article I give a Darwinian account of how sentience, consciousness and language may have evolved. It is argued that sentience and consciousness emerge as brains control purposive actions in new ways. A key feature of this account is that Darwinian theory is interpreted so as to do justice to the purposive character of living things. According to this interpretation, as evolution proceeds, purposive actions play an increasingly important role in the mechanisms of evolution until, with evolution (...) by cultural means, Darwinian evolution takes on a Lamarckian character. According to this view, as evolution proceeds, the mechanisms of Darwinian evolution themselves evolve. (shrink)

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding (...) misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)

We construct a machine that knows its own code, at the price of not knowing its own factivity.

3rd ed, 2021. A circumscription of the classical theory of computation building up from the Chomsky hierarchy. With the usual topics in formal language and automata theory.

It would be unwise to dismiss the possibility of human brain organoids developing sentience. However, scepticism about this idea is appropriate when considering current organoids. It is a point of consensus that a brainstem-dead human is not sentient, and current organoids lack a functioning brainstem. There are nonetheless troubling early warning signs, suggesting organoid research may create forms of sentience in the near future. To err on the side of caution, researchers with very different views about the neural (...) basis of sentience should unite behind the “brainstem rule”: if a neural organoid develops or innervates a functioning brainstem that registers and prioritizes its needs, regulates arousal, and leads to sleep-wake cycles, then it is a sentience candidate. If organoid research leads to the creation of sentience candidates, a moratorium or indefinite ban on the creation of the relevant type of organoid may be appropriate. A different way forward, more consistent with existing approaches to animal research, would be to require ethical review and harm-benefit analysis for all research on sentience candidates. (shrink)

Note: Paper to appear in special issue of the journal Psychology of Consciousness: Theory, Research, and Practice, on the evolution of consciousness //// The explanatory challenge of sentience is known as the “hard problem of consciousness”: How does subjective experience arise from physical objects and their relations? Despite some optimistic claims, the perennial struggle with this question shows little evidence of imminent resolution. In this article I focus on the “why” rather than on the “how” of sentience. Specifically, (...) why did sentience evolve in organic lifeforms? From an evolutionary perspective this question can be framed: “What adaptive problem(s) did organisms face in their evolutionary past and how were those challenges met? I argue that sentience was a critical component of the adaptive solution (i.e., adopting an agentic stance) to increasingly complex and unpredictable demands placed on vertebrates approximately 500 million years ago (the so-called Cambrian explosion). One consequence of taking an agentic stance is that it freed the organism from its neural moorings, positioning it within phenomenal space outside its brain. (shrink)

According to an orthodox view, the capacity for conscious experience (sentience) is relevant to the distribution of moral status and value. However, physicalism about consciousness might threaten the normative relevance of sentience. According to the indeterminacy argument, sentience is metaphysically indeterminate while indeterminacy of sentience is incompatible with its normative relevance. According to the introspective argument (by François Kammerer), the unreliability of our conscious introspection undercuts the justification for belief in the normative relevance of consciousness. I (...) defend the normative relevance of sentience against these objections. First, I demonstrate that physicalists only have to concede a limited amount of indeterminacy of sentience. This moderate indeterminacy is in harmony with the role of sentience in determining moral status. Second, I argue that physicalism gives us no reason to expect that introspection is unreliable with respect to the normative relevance of consciousness. (shrink)

Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why did Turing (...) refer to gender imitation in a test for machine intelligence? In this article, I shall address these questions by unveiling social, historical and epistemological roots of the so-called Turing test. I will draw attention to a historical fact that has been only scarcely observed in the secondary literature thus far, namely that Turing’s 1950 test emerged out of a controversy over the cognitive capabilities of digital computers, most notably out of debates with physicist and computer pioneer Douglas Hartree, chemist and philosopher Michael Polanyi, and neurosurgeon Geoffrey Jefferson. Seen in its historical context, Turing’s 1950 paper can be understood as essentially a reply to a series of challenges posed to him by these thinkers arguing against his view that machines can think. Turing did propose gender learning and imitation as one of his various imitation tests for machine intelligence, and I argue here that this was done in response to Jefferson's suggestion that gendered behavior is causally related to the physiology of sex hormones. (shrink)

Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the allocation (...) outcomes? If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance by k% of employers can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; (4) partial compliance based on local parity measures can induce extreme segregation. Finally, we discuss implications for auditors and insights concerning the design of regulatory frameworks. (shrink)

The creation of artificial moral systems requires us to make difficult choices about which of varying human value sets should be instantiated. The industry-standard approach is to seek and encode moral consensus. Here we argue, based on evidence from empirical psychology, that encoding current moral consensus risks reinforcing current norms, and thus inhibiting moral progress. However, so do efforts to encode progressive norms. Machine ethics is thus caught between a rock and a hard place. The problem is particularly acute (...) when progress beyond prevailing moral norms is particularly urgent, as is currently the case due to the inadequacy of prevailing moral norms in the face of the climate and ecological crisis. (shrink)

We welcome Mikhalevich & Powell’s (2020) (M&P) call for a more “‘inclusive”’ animal ethics, but we think their proposed shift toward a moral framework that privileges false positives over false negatives will require radically revising the paradigm assumption in animal research: that there is a clear line to be drawn between sentient beings that are part of our moral community and nonsentient beings that are not.

Genes are often described by biologists using metaphors derived from computa- tional science: they are thought of as carriers of information, as being the equivalent of ‘‘blueprints’’ for the construction of organisms. Likewise, cells are often characterized as ‘‘factories’’ and organisms themselves become analogous to machines. Accordingly, when the human genome project was initially announced, the promise was that we would soon know how a human being is made, just as we know how to make airplanes and buildings. Impor- tantly, (...) modern proponents of Intelligent Design, the latest version of creationism, have exploited biologists’ use of the language of information and blueprints to make their spurious case, based on pseudoscientific concepts such as ‘‘irreducible complexity’’ and on flawed analogies between living cells and mechanical factories. However, the living organ- ism = machine analogy was criticized already by David Hume in his Dialogues Concerning Natural Religion. In line with Hume’s criticism, over the past several years a more nuanced and accurate understanding of what genes are and how they operate has emerged, ironically in part from the work of computational scientists who take biology, and in particular developmental biology, more seriously than some biologists seem to do. In this article we connect Hume’s original criticism of the living organism = machine analogy with the modern ID movement, and illustrate how the use of misleading and outdated metaphors in science can play into the hands of pseudoscientists. Thus, we argue that dropping the blueprint and similar metaphors will improve both the science of biology and its understanding by the general public. (shrink)

In, “The Turing Triage Test”, published in Ethics and Information Technology, I described a hypothetical scenario, modelled on the famous Turing Test for machine intelligence, which might serve as means of testing whether or not machines had achieved the moral standing of people. In this paper, I: (1) explain why the Turing Triage Test is of vital interest in the context of contemporary debates about the ethics of AI; (2) address some issues that complexify the application of this test; (...) and, (3) in doing so, defend a way of thinking about the question of the moral standing of intelligent machines, which takes the idea of “seriousness” seriously. This last objective is, in fact, my primary one and is motivated by the sense that, to date, much of the “philosophy” of AI has suffered from a profound failure to properly distinguish between things that we can say and things that we can really mean. (shrink)