This paper provides a series of reflections on making the case to senior leaders for the introduction of clinical ethics support services within a UK hospital Trust at a time when clinical ethics committees are dwindling in the UK. The paper provides key considerations for those building a case for clinical ethics support within hospitals by drawing upon published academic literature, and key reports from governmental and professional bodies. We also include extracts from documents relating to, and annual (...) reports of, existing clinical ethics support within UK hospitals, as well as extracts from our own proposal submitted to the Trust Board. We aim for this paper to support other ethicists and/or health care staff contemplating introducing clinical ethics support into hospitals, to facilitate the process of making the case for clinical ethics support, and to contribute to the key debates in the literature around clinical ethics support. We conclude that there is a real need for investment in clinical ethics in the UK in order to build the evidence base required to support the wider introduction of clinical ethics support into UK hospitals. Furthermore, our perceptions of the purpose of, and perceived needs met through, clinical ethics support needs to shift to one of hospitals investing in their staff. Finally, we raise concerns over the optional nature of clinical ethics support available to practitioners within UK hospitals. (shrink)

The ability of very wealthy individuals (or, as I will call them, the ‘super-rich’) to turn their economic power into political power has been—and remains—an important cause of political inequality. In response, this paper advocates an original solution. Rather than solving the problem through implementing a comprehensive conception of political equality, or through enforcing complex rules about financial disclosure etc., I argue that we should impose a choice on the super-rich. The super-rich must choose between (i) forfeiting the things (...) that make them super-rich, i.e., pay a 100 % tax on their wealth above a certain level, or, (ii) they must forfeit some of their political rights. These rights include entitlements to fund political parties; to stand for office; and to work or volunteer for political parties. The right to vote, though, is not limited. I defend my proposal against non-consequentialist and consequentialist objections. I also argue that it avoids two problems that many attempts to reduce political inequality face; these are the political egalitarian’s dilemma and the problem of political equality’s relative moral importance. (shrink)

There has been criticism of how Fair-Trade products represent workers in remote parts of the world where packaging offers an encounter with distant others which romanticizes and homogenizes them as a pre-modern form of ethnicity. Such workers are shown as always engaged in authentic, simple, honest decontextualized manual labor. And they are depicted as highly appreciative of, and empowered by, the act of ethical shopping. This paper shows that a close social semiotic analysis of Fair-Trade packaging reveals a different (...) set of meanings which sit alongside the decontextualized ones. Designs integrate these workers into more contemporary kinds of modernist, rational, design chic, which communicates its own kind of honesty and authenticity. We consider how this, too, shapes how such consumers encounter distant others and its consequence for the meaning of the act of ethical shopping, where consumers can buy into moral alignment offered by products. (shrink)

The United Nations ‘Transforming our world: the 2030 Agenda for Sustainable Development’ lays out 17 Sustainable Development Goals to address a range of global issues related to the future of the planet and human well-being. Critics, however, argue that the Agenda, a complex product of multi-stakeholder governance, in its drive to accommodate many competing voices, is overloaded with weakly defined, overlapping and contradictory issues, concepts and buzzwords. These serve to gloss over actual concrete global problems and forces, concealing an underlying (...) free-trade ideology. In this paper, using Multimodal Critical Discourse Analysis, we draw attention to the nature of the documents used to communicate the Agenda. These documents comprise an edifice of self-referential texts that rely heavily on infographics, bullet points, charts and tables. Such formats appear to helpfully simplify, distil information and break things down into workable components. But, we show, through the affordances of these formats, the the vagueness of buzzwords, contradictions and lack of clear causalities can be glossed over, presenting the Agenda as a highly technical, engaging, and above all moral process. These formats are important, therefore, for the legitimization and rhetorical power of the Agenda, necessary for its take-up by governments and organizations around the world. (shrink)

Clinical ethics can be viewed as a practical discipline that provides a structured approach to assist healthcare practitioners in identifying, analysing and resolving ethical issues that arise in practice. Clinical ethics can therefore promote ethically sound clinical and organisational practices and decision-making, thereby contributing to health organisation and system quality improvement. In order to develop students’ decision-making skills, as well as prepare them for practice, we decided to introduce a clinical ethics strand within an undergraduate medical curriculum. We designed a (...) programme of clinical ethics activities for teaching and assessment purposes that involved using ethical frameworks to analyse hypothetical and real-life cases in uni- and inter- professional groups. In this paper, we draw on medical student feedback collected over 6 years to illustrate the appeal to students of learning clinical ethics. We also outline the range of benefits for students, healthcare organisations, and the field of clinical ethics arising from tomorrow’s doctors experiencing clinical ethics early in their training. We conclude by briefly reflecting on how including clinical ethics within tomorrow’s doctors curricular can secure and continue future engagement in clinical ethics support services in the UK, alongside the dangers of preparing students for organisational cultures that might not exist. We anticipate the findings presented in the paper will contribute to wider debates examining the impact of ethics teaching, and its ability to inform future doctors’ practice. (shrink)

It has become common to find diagrams and flow-charts used in our organizations to illustrate the nature of processes, what is involved and how it happens, or to show how parts of the organization interrelate to each other and work together. Such diagrams are used as they are thought to help visualization and simplify things in order to represent the essence of a particular situation, the core features. In this paper, using a social semiotic approach, we show that we (...) need to develop a much more critical sense of how these diagrams and flowcharts can easily abstract, conceal and substitute actual causalities, work roles and relationships. We demonstrate this using the example of a series of interrelated flows-charts used to implement a new system of target-based learning in preschool/kindergartens in Sweden – a system which works highly in favor of a rapidly privatizing education sector. Here, the flow charts shape how school processes and learning are presented to devalue the former system and value the new. (shrink)

Among the categories of the telecom and internet frauds, the online romance scam is of particular concern for its sharp rise of victim numbers and the huge amount of cost. A social semiotic approach could be used to investigate the victim identity of the online romance scam from the aspects of the (re)construction and interpretation of discursive practices. The range of papers in this section shows that the study of text, context and the way that people use semiotic resources to (...) produce communication, to create and manage events and to interpret them. (shrink)

ABSTRACT In this paper we explore how a public national media outlet, the British BBC, represents an international legal case which has a highly political nature. The case is US versus Huawei/meng Wanzhou, which took place between 2018 and 2021. Accusations were that the Chinese technology company committed fraud, leading the global HSBC bank to breach US sanctions against Iran. The charges were made by the US using what is called an ‘extraterritorial law’, which, while rejected as law by (...) governments around the world, is policed by US economic powers and control over international finance. Using Critical Discourse Analysis we show that, while the BBC presents much detail of legal process, the actual nature of the law the US uses to bring criminal charges against international companies and banks, is neither considered nor questioned. Our interest is how such a law, which has a huge influence over global trade and politics, is presented to the public in this particular case. We contribute to the position that the nature of laws, how they are used and known, must always be understood within the prevailing discourses of the moment. (shrink)

Clinical laboratories face ethical challenges on a daily basis. The ethics training provided for clinical laboratory staff is variable, with some receiving no training. We aimed to explore the perceived benefits of ethics education for laboratory professionals. Ethics training was provided to approximately 60 laboratory professionals in a UK not-for-profit blood cancer organisation, with group discussions incorporated into the session. The session covered dominant ethical theories and principles, the defining moments in medical research ethics and the ethical aspects of laboratory (...) practices. At the end of the session a short optional paper survey was distributed to the participants to obtain feedback on the training. The feedback was anonymous and thematically coded. Attendees reported to be more aware of the existence and importance of ‘everyday’ ethics in their workplace. Responses also showed that the training session had provided participants with an opportunity for ethical reflection in themselves and in discussion with their colleagues. Despite clinical laboratory professionals being faced with ethical challenges daily, there is comparatively little ethics education provided. Ethics training is believed to improve the ethical attitude of laboratory staff and help them when making decisions in their work. We have shown that ethics education is important for laboratory professionals to develop and retain ethical awareness, and ethical reflection. By gaining insight into the ethical aspects of their practices, laboratory professionals can apply this understanding when faced with making challenging decisions in their workplace, in order to act in the best interests of their patients. (shrink)

A growing body of literature critical of ethics review boards has drawn attention to the processes used to determine the ethical merit of research. Citing criticism on the bureaucratic nature of ethics review processes, this literature provides a useful provocation for (re)considering how the ethics review might be enacted. Much of this criticism focuses on how ethics review boards _deliberate,_ with particular attention given to the lack of transparency and opportunities for researcher recourse that characterise ethics review processes. Centered specifically (...) on the conduct of ethics review boards convened within university settings, this paper draws on these inherent criticisms to consider the ways that ethics review boards might enact more communicative and deliberative practices. Outlining a set of principles against which ethics review boards might establish strategies for engaging with researchers and research communities, this paper draws attention to how _Deliberative communication_, _Engagement with researchers_ and the _Distribution of responsibility_ for the ethics review might be enacted in the day-to-day practice of the university human ethics review board. This paper develops these themes via a conceptual lens derived from Habermas’ (The theory of communicative action. Volume 1: Reason and the rationalization of society, 1984 ) articulation of ‘communicative action’ and Fraser’s (Social Text, 25(26), 56–80, 1990 ) consideration of ‘strong publics’ to cast consideration of the role that human ethics review boards might play in supporting university research cultures. _Deliberative communication_, _Engagement with researchers_ and the _Distribution of responsibility_ provide useful conceptual prompts for considering how ethics review boards might undertake their work. (shrink)

This paper investigates the medical law and ethics learning needs of Foundation doctors by means of a national survey developed in association with key stakeholders including the General Medical Council and Health Education England. Four hundred sevnty-nine doctors completed the survey. The average self-reported level of preparation in MEL was 63%. When asked to rate how confident they felt in approaching three cases of increasing ethical complexity, more FYs were fully confident in the more complex cases than in the (...) more standard case. There was no apparent relationship with confidence and reported teaching at medical school. The less confident doctors were no more likely to ask for further teaching on the topic than the confident doctors. This suggests that FYs can be vulnerable when facing ethical decisions by being underprepared, not recognising their lack of ability to make a reasoned decision or by being overconfident. Educators need to be aware of this and provide practical MEL training based on trainee experiences and real-world ethics and challenge learners’ views. Given the complexities of many ethical decisions, preparedness should not be seen as the ability to make a difficult decision but rather a recognition that such cases are difficult, that doubt is permissible and the solution may well be beyond the relatively inexperienced doctor. Educators and supervisors should therefore be ensuring that this is clear to their trainees. This necessitates an environment in which questions can be asked and uncertainty raised with the expectation of a supportive response. (shrink)

This book questions the book itself, archivization, machines for writing, and the mechanicity inherent in language, the media, and intellectuals. Derrida questions what takes place between the paper and the machine inscribing it. He examines what becomes of the archive when the world of paper is subsumed in new machines for virtualization, and whether there can be a virtual event or a virtual archive. Derrida continues his long-standing investigation of these issues, and ties them into the new (...) themes that governed his teaching and thinking in the past few years: the secret, pardon, perjury, state sovereignty, hospitality, the university, animal rights, capital punishment, the question of what sort of mediatized world is replacing the print epoch, and the question of the “wholly other.” Derrida is remarkable at making seemingly occasional pieces into part of a complexly interconnected trajectory of thought. (shrink)

This article introduces Turing’s idea of a “paper machine” to identify and understand one important mode of clinical research in the modern hospital, how that research worked, and how office technology and industrialized labor shaped and helped drive it. The unusually rich archives of Berlin psychiatry allow detailed reconstruction of the making of the new diagnostic category “hyperkinetic syndrome” in the 1920s. From the generating of data to the processing of information to the visualizing of the nature and (...) course of the new syndrome in the lives of more than sixty patients, this case study shows how clinical research could be based on the apparatus of the clerks’ room (folders, registers, inventories, and the dispatch of documents), office technologies (new filing systems, preprinted forms, and duplicating machines), and the principles and paper practices of the division and rationalization of labor (charts organizing worktime in complex organizations). The result is an important example of clinical research embedded in the broader history of office technology, industrial labor, and the modern hospital. (shrink)

This book questions the book itself, archivization, machines for writing, and the mechanicity inherent in language, the media, and intellectuals. Derrida questions what takes place between the paper and the machine inscribing it. He examines what becomes of the archive when the world of paper is subsumed in new machines for virtualization, and whether there can be a virtual event or a virtual archive. Derrida continues his long-standing investigation of these issues, and ties them into the new (...) themes that governed his teaching and thinking in the past few years: the secret, pardon, perjury, state sovereignty, hospitality, the university, animal rights, capital punishment, the question of what sort of mediatized world is replacing the print epoch, and the question of the “wholly other.” Derrida is remarkable at making seemingly occasional pieces into part of a complexly interconnected trajectory of thought. (shrink)

Machines were introduced as calculating devices to simulate operations carried out by human computers following fixed algorithms. The mathematical study of (paper) machines is the topic of our essay. The first three sections provide necessary logical background, examine the analyses of effective calculability given in the thirties, and describe results that are central to recursion theory, reinforcing the conceptual analyses. In the final section we pursue our investigation in a quite different way and focus on principles that govern the (...) operations of physically realizable machines. (shrink)

Reason is often thought of as a fixed entity, as a definitive body of facts that do not change over time. But during the Enlightenment reason was also seen as a process, as a set of skills enacted on a daily basis. How, why, and where were these skills learned? Concentrating on the notebooks created by Scottish students over the course of the long eighteenth century, Matthew Eddy argues that notekeeping was a mode of writing and rewriting reason. He reveals (...) it as a capability-building exercise that enabled students to mobilize everyday forms of material culture in a way that empowered them to judge and enact the enlightened principles they encountered in the classroom. The cognitive skills required to make and use notebooks were not simply aids to reason-they were part of reason itself. The book begins by problematizing John Locke's comparison of the mind to a blank piece of paper, the tabula rasa. Although it is one of the most recognizable metaphors of the British Enlightenment, scholars seldom consider why it was so successful for those who used it. Eddy makes a case for using the material culture of early modern manuscripts to expand the meaning of the metaphor in a way that offers a clearer understanding of the direct relationship that notekeepers learned to draw between reasoning and notekeeping. Starting in the home, moving to schools, and then ending with universities, the rest of the book explores this argument by reconstructing the relationship from the bottom up. Media and the Mind will prove useful to those interested in book history, manuscript culture, history of education, history of childhood, Scottish Enlightenment philosophy, and the Enlightenment broadly understood. (shrink)

It is commonly thought that unconscious processes cannot produce actions deserving praise or blame. I present a thought experiment designed to generate a contradicting intuition: at least in this case, we do give credit for the product of an unconscious process. The target is creativity. Many instances of creative thought begin with a step that unconsciously generates a new idea by combining existing ideas. The resulting ideas are selected and developed by later processing. This first step could be replaced with (...) a simple machine that randomly pairs concepts. Now, imagine a philosopher, Liberty, who gets all of his paper ideas from this machine. Compare him to another philosopher, Libertad, who comes up with all the same papers using her own mind. If you share the intuition that Liberty’s work deserves less credit for creativity, you are giving credit for the product of an unconscious process. (shrink)

The aim of this paper is to present the idea which served as a prototype and probably a test field for the idea of the well-known Turing test. This idea is the ‘paper machine’ (an algorithm) for playing chess and the proposal to test its abil-ities in confrontation with a human chess player. I will describe the details of this proposal and discuss it in the light of the Turing test setting.

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)

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)

In the past few years, machine learning (ML) tools have been implemented with success in the medical context. However, several practitioners have raised concerns about the lack of transparency—at the algorithmic level—of many of these tools; and solutions from the field of explainable AI (XAI) have been seen as a way to open the ‘black box’ and make the tools more trustworthy. Recently, Alex London has argued that in the medical context we do not need machine learning tools (...) to be interpretable at the algorithmic level to make them trustworthy, as long as they meet some strict empirical desiderata. In this paper, we analyse and develop London’s position. In particular, we make two claims. First, we claim that London’s solution to the problem of trust can potentially address another problem, which is how to evaluate the reliability of ML tools in medicine for regulatory purposes. Second, we claim that to deal with this problem, we need to develop London’s views by shifting the focus from the opacity of algorithmic details to the opacity of the way in which ML tools are trained and built. We claim that to regulate AI tools and evaluate their reliability, agencies need an explanation of how ML tools have been built, which requires documenting and justifying the technical choices that practitioners have made in designing such tools. This is because different algorithmic designs may lead to different outcomes, and to the realization of different purposes. However, given that technical choices underlying algorithmic design are shaped by value-laden considerations, opening the black box of the design process means also making transparent and motivating (technical and ethical) values and preferences behind such choices. Using tools from philosophy of technology and philosophy of science, we elaborate a framework showing how an explanation of the training processes of ML tools in medicine should look like. (shrink)

Analogies to machines are commonplace in the life sciences, especially in cellular and molecular biology — they shape conceptions of phenomena and expectations about how they are to be explained. This paper offers a framework for thinking about such analogies. The guiding idea is that machine-like systems are especially amenable to decompositional explanation, i.e., to analyses that tease apart underlying components and attend to their structural features and interrelations. I argue that for decomposition to succeed a system must (...) exhibit causal orderliness, which I explicate in terms of differentiation among parts and the significance of local relations. I also discuss what makes a model depict its target as machine-like, suggesting that a key issue is the degree of detail with respect to the target’s parts and their interrelations. (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)

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)

Introduction. During the past two decades philosophers of psychology have considered a large variety of computational models for philosophy of mind and more recently for cognitive science. Among the suggested models are computer programs, Turing machines, pushdown automata, linear bounded automata, finite state automata and sequential machines. Many philosophers have found finite state automata models to be the most appealing, for various reasons, although there has been no shortage of defenders of programs and Turing machines. A paper by Arthur (...) Burks convinced me long ago that “all natural human functions” are, or can be fruitfully modeled to be, finite state automata with output. Further work in the field has reinforced this conviction. There is room, however, for the use of any of the above models in philosophy of mind and in the ongoing development of cognitive science. (shrink)

Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connections between machine learning and essentialism, but only for some kinds of machine learning, and often not including deep learning methods. Similarity-based approaches, more connected to (...) the overall prototype theory, spanning from psychology and linguistics, seem more suited for pattern recognition and complex deep-learning issues, while for classification problems, mostly for unsupervised learning, essentialism seems like the best choice. In order to illustrate the difference better, we will connect both paths to their sources in other disciplines and see how human psychology influences our decision in machine-learning modeling as well. This leads to a philosophically very interesting consequence: even in the setting of supervised machine learning, essences are not present in data, but in targets, which in turn means that the categories which purport to be essences are in fact human-made, and hand-coded in the targets. The success of machine learning, therefore, does not give any substantial evidence for the independent existence of essential properties. Our stance here is to state that neither the existence nor the lack of “essential” properties in machine learning can lead to metaphysical, i.e., ontological claims. (shrink)

Brain machine interface (BMI) technology makes direct communication between the brain and a machine possible by means of electrodes. This paper reviews the existing and emerging technologies in this field and offers a systematic inquiry into the relevant ethical problems that are likely to emerge in the following decades.

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)

Robert Nozick's experience machine thought experiment (Nozick's scenario) is widely used as the basis for a ?knockdown? argument against all internalist mental state theories of well-being. Recently, however, it has been convincingly argued that Nozick's scenario should not be used in this way because it elicits judgments marred by status quo bias and other irrelevant factors. These arguments all include alternate experience machine thought experiments, but these scenarios also elicit judgments marred by status quo bias and other irrelevant (...) factors. In this paper, several experiments are conducted in order to create and test a relatively bias-free experience machine scenario. It is argued that if an experience machine thought experiment is used to evaluate internalist mental state theories of well-being, then this relatively bias-free scenario should be used over any of the existing scenarios. Unlike the existing experience machine scenarios, when this new scenario is used to assess internalist mental state theories of well-being, it does not provide strong evidence to refute or endorse them. (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)

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)

In his famous 1950 paper where he presents what became the benchmark for success in artificial intelligence, Turing notes that "at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted" (Turing 1950, 442). Kurzweil (1990) suggests that Turing's prediction was correct, even if no machine has yet to pass the Turing Test. In the wake (...) of the computer revolution, research in artificial intelligence and cognitive science has pushed in the direction of interpreting "thinking" as some sort of computational process. On this understanding, thinking is something computers (in principle) and humans (in practice) can both do. It is difficult to say precisely when in history the meaning of the term "thinking" headed in this direction. Signs are already present in the mechanistic and mathematical tendencies of the early Modern period, and maybe even glimmers are apparent in the ancient Greeks themselves. But over the long haul, we somehow now consider "thinking" as separate from the categories of "thoughtfulness" (in the general sense of wondering about things), "insight" and "wisdom." Intelligent machines are all around us, and the world is populated with smart cars, smart phones and even smart (robotic) appliances. But, though my cell phone might be smart, I do not take that to mean that it is thoughtful, insightful or wise. So, what has become of these latter categories? They seem to be bygones left behind by scientific and computational conceptions of thinking and knowledge that no longer have much use for them. In 2000, Allen, Varner and Zinser addressed the possibility of a Moral Turing Test (MTT) to judge the success of an automated moral agent (AMA), a theme that is repeated in Wallach and Allen (2009).. (shrink)

As machines take over more tasks previously done by humans, artistic creation is also considered as a candidate to be automated. But, can machines create art? This paper offers a conceptual framework for a philosophical discussion of this question regarding the status of machine art and machine creativity. It breaks the main question down in three sub-questions, and then analyses each question in order to arrive at more precise problems with regard to machine art and (...) class='Hi'>machine creativity: What is art creation? What do we mean by art? And, what do we mean by machines create art? This then provides criteria we can use to discuss the main question in relation to particular cases. In the course of the analysis, the paper engages with theory in aesthetics, refers to literature on computational creativity, and contributes to the philosophy of technology and philosophical anthropology by reflecting on the role of technology in art creation. It is shown that the distinctions between process versus outcome criteria and subjective versus objective criteria of creativity are unstable. It is also argued that we should consider non-human forms of creativity, and not only cases where either humans or machines create art but also collaborations between humans and machines, which makes us reflect on human-technology relations. Finally, the paper questions the very approach that seeks criteria and suggests that the artistic status of machines may be shown and revealed in the human/non-human encounter before any theorizing or agreement takes place; an experience which then is presupposed when we theorize. This hints at a more general model of what happens in artistic perception and engagement as a hybrid human-technological and emergent or even poetic process, a model which leaves more room for letting ourselves be surprised by creativity—human and perhaps non-human. (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)

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)

The Internet is an important focus of attention for the philosophy of mind and cognitive science communities. This is partly because the Internet serves as an important part of the material environment in which a broad array of human cognitive and epistemic activities are situated. The Internet can thus be seen as an important part of the ‘cognitive ecology’ that helps to shape, support and realize aspects of human cognizing. Much of the previous philosophical work in this area has sought (...) to analyze the cognitive significance of the Internet from the perspective of human cognition. There has, as such, been little effort to assess the cognitive significance of the Internet from the perspective of ‘machine cognition’. This is unfortunate, because the Internet is likely to exert a significant influence on the shape of machine intelligence. The present paper attempts to evaluate the extent to which the Internet serves as a form of cognitive ecology for synthetic forms of intelligence. In particular, the phenomenon of Internet-situated machine intelligence is analyzed from the perspective of a number of approaches that are typically subsumed under the heading of situated cognition. These include extended, embedded, scaffolded and embodied approaches to cognition. For each of these approaches, the Internet is shown to be of potential relevance to the development and operation of machine-based cognitive capabilities. Such insights help us to appreciate the role of the Internet in advancing the current state-of-the-art in machine intelligence. (shrink)

This paper develops an account of how the implementation of ML models into healthcare settings requires revising the methodological apparatus of philosophical bioethics. On this account, ML models are cognitive interventions that provide decision-support to physicians and patients. Due to reliability issues, opaque reasoning processes, and information asymmetries, ML models pose inferential problems for them. These inferential problems lay the grounds for many ethical problems that currently claim centre-stage in the bioethical debate. Accordingly, this paper argues that the (...) best way to make progress in remedying these ethical problems is to distil their epistemic core and to identify appropriate epistemic norms as guardrails. The viability of this approach will be highlighted based on four key issues: trust, responsibility, paternalism, and fairness. In that respect, the paper also contributes to a more pronounced view of the specific methodological challenges for ethical theorizing, when the point of reference are complex statistical models that make predictions, rather than individual agents, acting for reasons. (shrink)

Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects (...) of outputs on subsequent inputs to achieve good performance over episodes of interaction with their environments. Supervised learning systems, in contrast, merely learn to produce better outputs in response to individual inputs. (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)

In Weaving the Web, Berners-Lee defines Social Machines as biotechnologically hybrid Web-processes on the basis of which, “high-level activities, which have occurred just within one human’s brain, will occur among even larger more interconnected groups of people acting as if the shared a larger intuitive brain”. The analysis and design of Social Machines has already started attracting considerable attention both within the industry and academia. Web science, however, is still missing a clear definition of what a Social Machine is, (...) which has in turn resulted in several calls for a “philosophical engineering” ; Halpin et al. 2010). This paper is a first attempt to respond to this call, by combining contemporary philosophy of mind and cognitive science with epistemology. The idea of philosophical engineering implies that a sufficiently good conception of Social Machines should be of both theoretical and practical advantage. To demonstrate how the present approach can satisfy both objectives it will be used in order to address one of Wikipedia’s most worrying concerns—i.e., the current and ongoing decline in the number of its active contributors. (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)

Instead of using low-level neurophysiology mimicking and exploratory programming methods commonly used in the machine consciousness field, the hierarchical Operational Architectonics (OA) framework of brain and mind functioning proposes an alternative conceptual-theoretical framework as a new direction in the area of model-driven machine (robot) consciousness engineering. The unified brain-mind theoretical OA model explicitly captures (though in an informal way) the basic essence of brain functional architecture, which indeed constitutes a theory of consciousness. The OA describes the neurophysiological basis (...) of the phenomenal level of brain organization. In this context the problem of producing man-made “machine” consciousness and “artificial” thought is a matter of duplicating all levels of the operational architectonics hierarchy (with its inherent rules and mechanisms) found in the brain electromagnetic field. We hope that the conceptual-theoretical framework described in this paper will stimulate the interest of mathematicians and/or computer scientists to abstract and formalize principles of hierarchy of brain operations which are the building blocks for phenomenal consciousness and thought. (shrink)

Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. (...) From the current literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively. (shrink)