Cora Diamond has claimed that Wittgenstein’s Tractatus contains an early ‘private language argument’: an argument that private objects in other people’s minds can play no role in the language I use for talking about their sensations. She further claims that the Tractatus contains an early version of the later idea that an inner process stands in need of outward criteria. The paper argues against these claims, on the grounds that they depend on an unwarranted construal of the Tractatus’s notion of (...) use. It is further argued that Diamond’s interpretation makes a mystery of the relation between the Tractatus and Wittgenstein’s 1929 account of sensation language, set out in Philosophical Remarks and elsewhere. Finally, the paper considers and defends Michael Dummett’s contention that the Tractatus is a paradigm of semantic realism, in the light of Diamond’s claim that the Tractatus in fact suggests a form of anti-realism about sensation language. (shrink)

What is the relation between meaning and use? This chapter first defends a non-reductionist understanding of Wittgenstein’s suggestion that ‘the meaning of a word is its use in the language’; facts about meaning cannot be reduced to, or explained in terms of, facts about use, characterized non-semantically. Nonetheless, it is contended, facts about meaning do supervene on non-semantic facts about use. That supervenience thesis is suggested by comments of Wittgenstein’s and is consistent with his view of meaning and rule-following. Semantic (...) supervenience is then defended against two criticisms: first, John McDowell’s suggestion that the supervenience thesis falsifies the epistemology of meaning and fails to accommodate common-sense truths about meaning; second, a series of counter-examples proposed by Stephen Kearns and Ofra Magidor, who argue that worlds may differ semantically without differing non-semantically. It is argued that neither criticism is convincing: we should accept the thesis that semantic facts supervene on non-semantic facts. (shrink)

It is natural to say that when we acquire a new concept or concepts, or grasp a new theory, or master a new practice, we come to see things in a new way: we perceive phenomena that we were not previously aware of; we come to see patterns or connections that we did not previously see. That natural idea has been applied in many areas, including the philosophy of science, the philosophy of religion, and the philosophy of language. And, in (...) reflecting on the character of philosophy itself, Wittgenstein himself associates the introduction of a new concept in philosophy with the discovery of a new way of looking at things (PI §401), and says that the point of presenting us with an imagined example may be to change our way of looking at things (PI §144). The paper considers four interrelated questions about this association between seeing-as and change, novelty, or innovation. 1. Is there an important difference between the case where the change in someone’s way of seeing things involves the invention or discovery of a completely new concept, a completely new theory etc., and the case where it involves the person’s coming to understand an existing concept or theory? 2. Is there a special connection between seeing-as and conceptual or theoretical change or creativity: a connection that does not obtain between seeing-as and using or applying a familiar theory or system of concepts? 3. In cases where seeing-as is associated with novelty or creativity, is there any sense in which the fact that someone sees things in a new way can help to explain their conceptual or theoretical creativity? 4. To the extent that there is some association between seeing-as and novelty or creativity, why if at all does it matter that one experiences things in a certain way? Would someone who was aspect-blind be any worse off with respect to creativity or innovation than the rest of us? And if so, why? (shrink)

A central theme in Wittgenstein’s post-Tractatus remarks on the limits of language is that we ‘cannot use language to get outside language’. One illustration of that idea is his comment that, once we have described the procedure of teaching and learning a rule, we have ‘said everything that can be said about acting correctly according to the rule’; ‘we can go no further’. That, it is argued, is an expression of anti-reductionism about meaning and rules. A framework is presented for (...) assessing the debate between reductionist and anti-reductionist readings of Wittgenstein’s views about meaning and use. It is argued that that debate cannot be settled merely by reference to Wittgenstein’s general opposition to reductionism. An important argument for anti-reductionism about rules and meaning, from Remarks on the Foundations of Mathematics, is discussed. Putative evidence of reductionism about meaning in the Brown Book is considered; an alternative reading is proposed. The nature of Wittgenstein’s anti-reductionism is examined. It is argued, first, that Wittgenstein accepts that semantic and normative facts supervene on non-semantic, non-normative facts and, second, that at many points his treatment of meaning and rules goes beyond the kind of pleonastic claim that is often taken to define non-reductionist, quietist, positions in philosophy. (shrink)

The paper considers three questions. First, what is the connection between economics and agency? It is argued that causation and explanation in economics fundamentally depend on agency. So a philosophical understanding of economic explanation must be sensitive to an understanding of agency. Second, what is the connection between agency and causation? A causal view of agency-involving explanation is defended against a number of arguments from the resurgent noncausalist tradition in the literature on agency and action-explanation. If agency is fundamental to (...) economic explanation, it is argued, then so is causation. Third, what is the connection between causal explanation and the natural sciences? It is argued that, though the explanations given in economics and other social sciences are causal explanations, they are different in kind from the causal explanations of the natural sciences. On the one hand, then, the causal explanations of the social sciences are irreducible to those found in the natural sciences. On the other hand, the causal relations described by the social sciences are not completely autonomous; they do not float free of, or operate independently from, the causal relations charted by the natural sciences. (shrink)

Part 1 of this paper sketches Wittgenstein’s opposition to scientism in general. Part 2 explores his opposition to scientism in philosophy focusing, in particular, on philosophy of mind; how must philosophy of mind proceed if it is to avoid the kind of scientism that Wittgenstein complains about? Part 3 examines a central anti-scientistic strand in Wittgenstein’s Last Writings on the Philosophy of Psychology volume II: his treatment of the ‘uncertainty’ of the relation between ‘outer’ behaviour and ‘inner’ experiences and mental (...) states. (shrink)

Wittgenstein’s philosophy involves a general anti-platonism about properties or standards of similarity. On his view, what it is for one thing to have the same property as another is not dictated by reality itself; it depends on our classificatory practices and the standards of similarity they embody. Wittgenstein’s anti-platonism plays an important role in the private language sections and in his discussion of the conceptual problem of other minds. In sharp contrast to Wittgenstein’s views stands the contemporary doctrine of natural (...) properties, which holds that there is an objective hierarchy of naturalness amongst properties, a hierarchy that is completely independent of our concepts or practices. Some authors have appealed to the natural properties view to offer an explicitly anti-Wittgensteinian account of sensation concepts. The paper discusses these competing views of properties and sensation concepts. It is argued that, if our account of concepts of conscious states starts from a commitment to natural properties, we are bound to recognize that our actual classificatory practices also play a crucial role in determining which properties our concepts pick out. On the other hand, if we start from the anti-platonist position, we are bound to recognize that we also need a notion of sameness of property that extends beyond our limited capacity to recognize similarity or sameness of property. The correct view, it is concluded, must occupy a middle position between an extreme anti-realism about properties and an extreme version of the natural properties view. It is suggested that Wittgenstein’s own view does just that. (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)

The paper discusses two aspects of Wittgenstein’s middle-period discussions of the self and the use of ‘I’. First, it considers the distinction Wittgenstein draws in his 1933 Cambridge lectures between two ‘utterly different’ uses of the word ‘I’. It is shown that Wittgenstein’s discussion describes a number of different and non-equivalent distinctions between uses of ‘I’. It is argued that his claims about some of these distinctions are defensible but that his reasoning in other cases is unconvincing. Second, the paper (...) considers the distinction drawn in the Blue Book between the use of ‘I’ as subject and the use of ‘I’ as object. A number of commentators have contended that this Blue Book distinction between uses of ‘I’ is erroneous, that Wittgenstein soon realized that, and that he dropped the idea of such a distinction from his later work. Against those claims, it is argued that Wittgenstein’s distinction between the use of ‘I’ as subject and its use as object is correct and illuminating. And it is shown that, though we do not find the ‘as-subject’/’as-object’ terminology in Wittgenstein’s subsequent work, the essential point of the Blue Book distinction is not abandoned but remains in place in Philosophical Investigations. (shrink)

Turing used the expression “emotional” in three distinct ways: to state his philosophical theory of the concept of intelligence, to classify arguments for and against the possibility of machine intelligence, and to describe the education of a “child machine”. The remarks on emotion include several of the most important philosophical claims. This paper analyses these remarks and their significance for current research in Artificial Intelligence.

Memory is everywhere in Blade Runner 2049. From the dead tree that serves as a memorial and a site of remembrance (“Who keeps a dead tree?”), to the ‘flashbulb’ memories individuals hold about the moment of the ‘blackout’, when all the electronic stores of data were irretrievably erased (“everyone remembers where they were at the blackout”). Indeed, the data wiped out in the blackout itself involves a loss of memory (“all our memory bearings from the time, they were all damaged (...) in the blackout”). Memory, and lack of it, permeates place, where from the post-blackout Las Vegas Deckard remembers it as somewhere you could “forget your troubles.” Memory is a commodity, called upon and consumed by the Wallace Corporation, purchased from the memory-maker, Dr Ana Stelline, who constructs and implants “the best memories” in replicants so as to instil in them real human responses. Memory is ubiquitous in Blade Runner 2049, involving humans, replicants, objects, and machines. Even “God,” we are told, “remembered Rachael.” Nowhere, though, is the depiction of memory more important than in the attempt to solve a question of identity. Officer K has a memory of his past. Even though he knows it is an implant, it is a memory he is emotionally attached to, frequently narrating it to Joi, his digital girlfriend. But it is a memory that starts to puzzle and trouble him. When K discovers the remains of a dead replicant, a female NEXUS-7, he uncovers a secret—this replicant was pregnant and died during childbirth, a discovery that could “break the world.” K is charged with hunting down the child and making the problem disappear. Yet as K starts seeking answers to the question of the child’s identity he gets inextricably caught up in the mystery. Is he merely Officer K, or is he Joe, the miracle child of Rachael and Deckard? The answer to this question hinges on K’s memory. But is the memory genuine? Is the memory his? Blade Runner 2049 encourages us to think deeply about the nature of memory, identity, and the relation between them. Indeed, the film does not just serve as a starting point for thinking about philosophical issues related to memory and identity. Rather, as we show in this chapter, the film seems to offer a view on these philosophical issues. Blade Runner 2049 offers us a view of memory as spread out over people, objects, and the environment, and it shows us that memory’s role in questions of identity goes beyond merely accurately recalling one’s past. Identity depends not on memory per se, but partly on what we use memory for. (shrink)

The notion of a mental interior has been derided as a Cartesian relic, the 'ghost in the machine' (Ryle, 1963). Yet there is a mental interior — indeed, there are two — only not where we tend to look. When a toddler talks to herself before sleep, she often plays the part of a parent toward herself, mitigating the dread of separation. She thus creates a pretend space between herself-as-parent and herself-as-child. Growing up, she plays others toward herself (...) as well. She and her simulated interlocutors are experienced by her as an expanded self with an inside, namely the place of inner speech. This pretend space is the first non-bodily interior. The second develops as a consequence. The simulated others diminish the dependence on actual others, who therefore cease to appear in their former importance. One yearns for them as they were, but the yearning is blocked — and banished from awareness — by dread of reverting to the earlier dependence. This second interior is the space between the unconscious self and the full kind of other for whom it yearns. The yearning enters conscious life indirectly and unthreateningly, for example in the fictive frame of art and in the relational frame of conversation. (shrink)

Robots as social companions in close proximity to humans have a strong potential of becoming more and more prevalent in the coming years, especially in the realms of elder day care, child rearing, and education. As human beings, we have the fascinating ability to emotionally bond with various counterparts, not exclusively with other human beings, but also with animals, plants, and sometimes even objects. Therefore, we need to answer the fundamental ethical questions that concern human-robot-interactions per se, and we (...) need to address how we conceive of »good lives«, as more and more of the aspects of our daily lives will be interwoven with social robots. (shrink)

As art produced by autonomous machines becomes increasingly common, and as such machines grow increasingly sophisticated, we risk a confusion between art produced by a person but mediated by a machine, and art produced by what might be legitimately considered a machine artist. This distinction will be examined here. In particular, my argument seeks to close a gap between, on one hand, a philosophically grounded theory of art and, on the other hand, theories concerned with behavior, intentionality, expression, (...) and creativity in natural and artificial agents. This latter set of theories in some cases addresses creative behavior in relation to visual art, music, and literature, in the frequently overlapping contexts of philosophy of mind, artificial intelligence, and cognitive science. However, research in these areas does not typically address problems in the philosophy of art as a central line of inquiry. Similarly, the philosophy of art does not typically address issues pertaining to artificial agents. (shrink)

Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning---as far as they are concerned with reliability.

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)

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)

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)

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)

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)

Parenting involves an extraordinary degree of power over children. Republicans are concerned about domination, which, on one view, is the holding of power that fails to track the interests of those over whom it is exercised. On this account, parenting as we know it is dominating due to the low standards necessary for acquiring and retaining parental rights and the extent of parental power. Domination cannot be fully eliminated from child-rearing without unacceptable loss of value. Most likely, republicanism requires (...) that we minimise children’s domination. I examine alternative models of child-rearing that are immune to republican criticism. (shrink)

In July 2014, the roboticist Ronald Arkin suggested that child sex robots could be used to treat those with paedophilic predilections in the same way that methadone is used to treat heroin addicts. Taking this onboard, it would seem that there is reason to experiment with the regulation of this technology. But most people seem to disagree with this idea, with legal authorities in both the UK and US taking steps to outlaw such devices. In this paper, I subject (...) these different regulatory attitudes to critical scrutiny. In doing so, I make three main contributions to the debate. First, I present a framework for thinking about the regulatory options that we confront when dealing with child sex robots. Second, I argue that there is a prima facie case for restrictive regulation, but that this is contingent on whether Arkin’s hypothesis has a reasonable prospect of being successfully tested. Third, I argue that Arkin’s hypothesis probably does not have a reasonable prospect of being successfully tested. Consequently, we should proceed with utmost caution when it comes to this technology. (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)

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)

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

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)

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)

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.

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)

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)

There is broad agreement that child labor is wrong and should be eliminated. This chapter examines the three main moral objections to child labor and considers their limitations: harm-based objections, objections from failing to benefit children, and objections from exploitation. Harm-based objections struggle with baselines for comparison and difficulties with Non-Identity problems. Even if child labor is not harmful, it may be wrong because it prevents children from enjoying other benefits, such as schooling. However, is schooling necessarily (...) more beneficial for children than work? Some kinds of child labor may not be harmful, and may benefit children, but they may still be wrong because they are exploitative. I consider some ways to address exploitative child labor, inspired by campaigns led by child workers. The chapter ends with a surprising conclusion that challenges the broad consensus with which we began: we may have duties to promote child labor. (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)

Child soldiers, who often appear to be both victims and perpetrators, present a vexing moral and legal challenge: how can we protect the rights of children while seeking justice for the victims of war crimes? There has been little stomach, either in domestic or international courts, for prosecuting child soldiers—but neither has this challenge been systematically addressed in international law. Establishing a uniform minimum age of criminal responsibility would be a major step in the right direction; we argue (...) that such a standard ought to be guided by the best evidence from neuropsychology about the development, during childhood and adolescence, of the executive functions that give rise to morally and legally responsible agents. In light of that evidence, which suggests that the brain’s executive functions are still maturing into early adulthood, we recommend a graded structure of culpability for child soldiers. (shrink)

The global response to child labour is based on the standards set by three major international conventions. This review examines the historical development of the conceptualizations of various forms of child labour, relevant views and perspectives, contemporary theoretical underpinnings, and policy suggestions. The emerging evidence shows that child labour incidences in all its forms have increased in many parts of the world, and the global target to eradicate child labour by 2025 seems unattainable. The evaluation indicates (...) that the current global age-based abolitionist policy to fight child labour has lost some ground. The covid-19 pandemic has worsened the situation and the worst forms of child labour have become even more widespread and deeply normalized in many contexts and communities. The current scholarship of child labour remains critically ignorant of the relevant societal and cultural norms. Contemporary theorists and empiricists emphasize on constructing knowledge with the children and families engaged in child labour and focusing on finding innovative community-led alternatives to the worst forms of child labour. Regulations, policies, and support programmes must recognize the economic contribution of working children and work towards the children's best interests. (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)

In contemporary healthcare, medical image categorization is essential for illness prediction, diagnosis, and therapy planning. The emergence of digital imaging technology has led to a significant increase in research into the use of machine learning (ML) techniques for the categorization of images in medical data. We provide a thorough summary of recent developments in this area in this review, using knowledge from the most recent research and cutting-edge methods.We begin by discussing the unique challenges and opportunities associated with medical (...) image classification, including the complexity of anatomical structures, variability in imaging modalities, and the need for interpretability and reliability in clinical settings. Subsequently, we survey a wide range of ML algorithms and techniques employed for medical image classification, including traditional methods such as support vector machines and k-nearest neighbors, as well as deep learning approaches like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Furthermore, we examine key considerations in dataset preparation, feature extraction, and model evaluation specific to medical image classification tasks. We highlight the importance of large, annotated datasets, transfer learning, and data augmentation techniques in enhancing model performance and generalization. (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)

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)

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.

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)

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)

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)

Autonomous systems are machines that can alter their behavior without direct human oversight or control. How ought we to program them to behave? A plausible starting point is given by the Reduction to Acts Thesis, according to which we ought to program autonomous systems to do whatever a human agent ought to do in the same circumstances. Although the Reduction to Acts Thesis is initially appealing, we argue that it is false: it is sometimes permissible to program a machine (...) to do something that it would be wrong for a human to do. We advance two main arguments for this claim. First, the way an autonomous system will behave can be known in advance. This knowledge can indirectly affect the behavior of other agents, while the same may not be true at the time the system actually executes its programming. Second, a lack of knowledge of the identities of the victims and beneficiaries can provide a justification during the programming phase that would be unavailable to an agent at the time the autonomous system executes its programming. (shrink)

I occasionally write on topics relating to psychology since I am a trained psychoanalyst. One of the evils which plagues us is child abuse which a psychologist had correctly called soul murder in the 1990s. This article was written to sensitize parents. And also is philosophy (of evil) in praxes.

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)

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.

The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that conceived (...) the computer. The hypostatized role of computers explains the increased dependence on them. The convergence machine called deep learning is only the most recent form through which the deterministic theology of the machine claims more than what it actually is: extremely effective data processing. A proper understanding of complexity, as well as the need to distinguish between the reactive nature of the artificial and the anticipatory nature of the living are suggested as practical responses to the challenges posed by machine theology. (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)

Theorem (Will time machines be build?). The probability that in future a time machine that can travel to the past would be build is very low.