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)

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)

Deals with topics and insight into the design and architecture of next-generation computing systems that deal with human and social problems. Processor and Internet technologies form the subject matter and focal point, while information and knowledge on the Internet delivered by next-generation mobile networks form the technical core presented.

A system of system’s ability to function is derived from the integration of systems from different sources. An SOS’s systems serve two purposes: first, to accomplish their own specific aims, and second, to provide resources to the SOS as a whole. In the last few decades, machine learning and data analytics have been widely used in system design and acquisitions. Every organisation that acquires a sophisticated system employs some type of data analytics to evaluate the system’s independent objectives, (...) which is universally accepted. Data analytics and decision-making regarding the independent system is rarely shared across SOS stakeholders, even though the systems contribute to and benefit from the larger SOS. The goal of this research is to determine how the exchange of data sets and the corresponding analytics by SOS stakeholders can improve SOS capacity. Predicting SOS capabilities by exchanging relevant data sets and prescribing information connections between systems, we propose to use machine learning techniques. This article serves as an intermediate analysis of the above research work and aims to estimate the benefit of information sharing among the SOS stakeholders. In this research, we have applied different machine learning models to the IBM HR analytics data set to determine the corresponding analytics by SOS stakeholders that can improve SOS capacity. We propose using machine learning techniques to forecast SOS capabilities through the sharing of relevant data sets, and we prescribe the information linkages across systems to make this possible. This paper provides an update on the progress being made toward the aforementioned research project, and its primary focus is on developing a method to put a dollar amount on the benefits of information sharing among the many parties involved in the SOS. (shrink)

Large amount of data are exchanged and the internet is turning into twenty-first century Silk Road for data. Machine learning (ML) is the new area for the applications. The artificial intelligence (AI) is the field providing machines with intelligence. In the last decades, more developments have been made in the field of ML and deep learning. The technology and other advanced algorithms are implemented into more computational constrained devices. The online English test system based on ML breaks the shackles (...) of the traditional paper English test and improves the efficiency of the English test. At the same time, it also maintains the fairness of English test and improves the marking speed. In order to realize an online English test system based on ML and facilitate the assessment of students’ college English courses, this paper mainly adopts relevant research and design on the main functional modules, key technologies, and functional realization of the online English test. The brand-new powerful teaching software and the online examination system can help schools to conduct more systematic and scientific management. The conclusion shows that as brand-new and powerful teaching software, the online examination system can help schools to conduct more systematic and scientific management. (shrink)

The rapid pace of development over the last few decades in the domain of machine learning mirrors the advances made in the field of quantum computing. It is natural to ask whether the conventional machine learning algorithms could be optimized using the present-day noisy intermediate-scale quantum technology. There are certain computational limitations while training a machine learning model on a classical computer. Using quantum computation, it is possible to surpass these limitations and carry out such calculations in (...) an optimized manner. This study illustrates the working of the quantum support vector machine classification model which guarantees an exponential speed-up over its typical alternatives. This research uses the quantum SVM model to solve the classification task of a malignant breast cancer diagnosis. This study also demonstrates a comparative analysis of distinct forms of SVM algorithms concerning their time complexity and performances on standard evaluation metrics, namely accuracy, precision, recall, and F1-score, to exemplify the supremacy of quantum SVM over its conventional variants. (shrink)

Given the pervasiveness of AI systems and their potential negative effects on people’s lives (especially among already marginalised groups), it becomes imperative to comprehend what goes on when an AI system generates a result, and based on what reasons, it is achieved. There are consistent technical efforts for making systems more “explainable” by reducing their opaqueness and increasing their interpretability and explainability. In this paper, we explore an alternative non-technical approach towards explainability that complement existing ones. Leaving aside technical, statistical, (...) or data-related issues, we focus on the very conceptual underpinnings of the design decisions made by developers and other stakeholders during the lifecycle of a machine learning project. For instance, the design and development of an app to track snoring to detect possible health risks presuppose some picture or another of “health”, which is a key notion that conceptually underpins the project. We take it as a premise that these key concepts are necessarily present during design and development, albeit perhaps tacitly. We argue that by providing “justificatory explanations” about how the team understands the relevant key concepts behind its design decisions, interested parties could gain valuable insights and make better sense of the workings and outcomes of systems. Using the concept of “health”, we illustrate how a particular understanding of it might influence decisions during the design and development stages of a machine learning project, and how making this explicit by incorporating it into ex-post explanations might increase the explanatory and justificatory power of these explanations. We posit that a greater conceptual awareness of the key concepts that underpin design and development decisions may be beneficial to any attempt to develop explainability methods. We recommend that “justificatory explanations” are provided as technical documentation. These are declarative statements that contain at its simplest: (1) a high-level account of the understanding of the relevant key concepts a team possess related to a project’s main domain, (2) how these understandings drive decision-making during the life-cycle stages, and (3) it gives reasons (which could be implicit in the account) that the person or persons doing the explanation consider to have plausible justificatory power for the decisions that were made during the project. (shrink)

As a first step in involving user emotion in human‐computer interaction, a memory‐based expert system (JANUS; Kearney, 1991) was designed to interpret facial expression in terms of the signaled emotion. Anticipating that a VDU‐mounted camera will eventually supply face parameters automatically, JANUS now accepts manually made measurements on a digitized full‐face photograph and returns emotion labels used by college students. An intermediate representation in terms of face actions (e.g., mouth open) is also used. Production rules convert the geometry into these. (...) A dynamic memory (Kolodner, 1984; Schank, 1982) interprets the face actions in terms of emotion labels. The memory is dynamic in the sense that new emotion labels can be learned with experience. A prototype system has been implemented on a Sun 2/120 system using POPLOG. Validation studies on the prototype suggest that the interpretations achieved are generally consistent with those of college students without formal instruction in emotion signals. (shrink)

Robot ethics encompasses ethical questions about how humans should design, deploy, and treat robots; machine morality encompasses questions about what moral capacities a robot should have and how these capacities could be computationally implemented. Publications on both of these topics have doubled twice in the past 10 years but have often remained separate from one another. In an attempt to better integrate the two, I offer a framework for what a morally competent robot would look like and discuss (...) a number of ethical questions about the design, use, and treatment of such moral robots in society. Instead of searching for a fixed set of criteria of a robot’s moral competence I identify the multiple elements that make up human moral competence and probe the possibility of designing robots that have one or more of these human elements, which include: moral vocabulary; a system of norms; moral cognition and affect; moral decision making and action; moral communication. Juxtaposing empirical research, philosophical debates, and computational challenges, this article adopts an optimistic perspective: if robotic design truly commits to building morally competent robots, then those robots could be trustworthy and productive partners, caretakers, educators, and members of the human community. Moral competence does not resolve all ethical concerns over robots in society, but it may be a prerequisite to resolve at least some of them. (shrink)

In sociology, there has been a controversy about whether there is any essential difference between a human being and a tool, or if the tool–user relationship can be defined by co-actor symmetry. This issue becomes more complex when we consider examples of AI and robots, and even more so following progress in the development of various bio-machine hybrid technologies, such as robots that include organic parts, human brain implants, and adaptive prosthetics. It is argued that a concept of autonomous (...) agency based on organismic embodiment helps to clarify the situation. On this view, agency consists of an asymmetrical relationship between an organism and its environment, because the continuous metabolic and regulatory activity of the organism gives rise to its own existence, and hence its specific behavioral domain. Accordingly, most of current technologies are excluded from the class of autonomous agents. Instead, they are better conceptualized as interfaces that mediate our interactions with the world. This has important implications for design: Rather than trying to help humans to achieve their goals by duplicating their agency in artificial systems, it would be better to empower humans directly by enhancing their existing agency and lived experience with technological interfaces that can be incorporated into their embodiment. This incorporation might be especially facilitated by bio-machine hybrid technology that is designed according the principles of biological autonomy and multi-agent coordination dynamics. (shrink)

We can learn about human ethics from machines. We discuss the design of a working machine for making ethical decisions, the N-Reasons platform, applied to the ethics of robots. This N-Reasons platform builds on web based surveys and experiments, to enable participants to make better ethical decisions. Their decisions are better than our existing surveys in three ways. First, they are social decisions supported by reasons. Second, these results are based on weaker premises, as no exogenous expertise (aside (...) from that provided by the participants) is needed to seed the survey. Third, N-Reasons is designed to support experiments so we can learn how to improve the platform. We sketch experimental results that show the platform is a success as well as pointing to ways it can be improved. (shrink)

When scientists attempt to explain observations of behaviour in humans and other animals, they often use language that evolved for informal discourse among people engaged in every day social interaction, like this: What does the infant/child/adult/chimp/crow perceive/understand/learn/intend? What is he/she/it conscious of? What does he/she/it experience/enjoy/desire? What is he/she/it attending to? Why did he/she/it do X, start Xing, stop Xing, speed up Xing...? Does he/she/it know that...? What did/does he/she/it expect will happen, if...? Similar comments can be made about the (...) terminology used in many philosophical discussions about minds, cognition, language, and the relationships between evolution and learning. (shrink)

When scientists discuss experimental observations, they often, unfortunately, use language that evolved for informal discourse among people engaged in every day social interaction, like this: What does the infant/child/adult/chimp/crow (etc) perceive/understand/learn/intend (etc)? What is he/she/it conscious of? What does he/she/it experience/enjoy/desire? What is he/she/it attending to? Similar comments can be made about the terminology used in many philosophical discussions about minds, cognition, language, and the relationships between evolution and learning.

Lethal Autonomous Weapons (LAWs) have becomes the subject of continuous debate both at national and international levels. Arguments have been proposed both for the development and use of LAWs as well as their prohibition from combat landscapes. Regardless, the development of LAWs continues in numerous nation-states. This paper builds upon previous philosophical arguments for the development and use of LAWs and proposes a design framework that can be used to ethically direct their development. The conclusion is that the philosophical (...) arguments that underpin the adoption of LAWs rather than not, although prima facie insufficient, can be actualized through the proposed Value Sensitive Design (VSD) approach. Hence, what is proposed is a principled design approach that can be used to embed stakeholder values into a design, encourage stakeholder cooperation and coordination and as a result promote social acceptance of LAWs as a preferable future fact of war. (shrink)

This paper suggests that the imbrication of user-friendly software and the posthuman has increasingly been revealed as an intrinsically arborescent relationship, one premised upon the striation of personal information through different forms of software media and allowing for unprecedented avenues of control and subjective manipulation. My analysis begins with a conceptualisation of user-friendliness, tracing its development as the majoritarian style of software design. In assessing the effects of this process of subjective imbrication with arborescent software technology, it is suggested (...) that one effect of the pathological asignification of instrumentality in the representation of software is the genesis of a topological space which allows for a capacity for control over the subjective becoming of others through control over digitally mediated perception. To illustrate this point, the revelations concerning Cambridge Analytica's use of targeted advertisements on Facebook to affect voting preferences during the 2016 American presidential election are examined as an example of the quotidian domination which is characteristic of the subjective condition of arbormosis. This analysis can be applied to issues of gender, technology, and modes of subjective control. (shrink)

The value sensitive design (VSD) approach to designing transformative technologies for human values is taken as the object of study in this chapter. VSD has traditionally been conceptualized as another type of technology or instrumentally as a tool. The various parts of VSD’s principled approach would then aim to discern the various policy requirements that any given technological artifact under consideration would implicate. Yet, little to no consideration has been given to how laws, regulations, policies and social norms engage (...) within VSD practices. Similarly, how the interactive nature of the VSD approach can, in turn, influence those directives. This is exacerbated when we consider machine ethics policy that have global consequences outside their development spheres. What constructs and models will position AI designers to engage in policy concerns? How can the design of AI policy be integrated with technical design? How might VSD be used to develop AI policy? How might law, regulations, social norms, and other kinds of policy regarding AI systems be engaged within value sensitive design? This chapter takes the VSD as its starting point and aims to determine how laws, regulations and policies come to influence how value trade-offs can be managed within VSD practices. It shows that the iterative and interactional nature of VSD both permits and encourages existing policies to be integrated both early on and throughout the design process. The chapter concludes with some potential future research programs. (shrink)

The Architecture Machine: Industrial Design and Theories of Racial Evolution -- The Bauhaus and the Aura of Authorship: Class and Race in the Age of Technological Reproducibility -- Post-War European Modernism: Or, What the Primitive Hut Really Said -- Bricolage, Megastructure, Interface: Yona Friedman and the Humanitarian Machine -- The Earth Unfolded: Aspen, Africa, and the Geopolitics of Environment -- Harlem Speaks to Aspen: Environmental Politics versus Environmental Design -- Pentecostal Technologies: The Architecture Machine (Again) (...) -- Technological Sovereignty and the Global South -- Conclusion: The Present and Future of Spiritual Ergonomics. (shrink)

Why do design arguments—particularly those emphasizing machine metaphors such as “Organisms and/or their parts are machines”—continue to be so convincing to so many people after they have been repeatedly refuted? In this essay I review various interpretations and refutations of design arguments and make a distinction between rationally refuting such arguments (RefutingR) and rendering them psychologically unconvincing (RefutingP). Expanding on this distinction, I provide support from recent work on the cognitive power of metaphors and developmental psychological work (...) indicating a basic human propensity toward attributing agency to natural events, to show that design arguments “make sense”unless one is cued to look more closely. As with visual illusions, such as the Müller-Lyer arrow illusion, there is nothing wrong with a believer's cognitive apparatus any more than with their visual apparatus when they judge the lines in the illusion to be of unequal length. It takes training or a dissonance between design beliefs and other beliefs or experiences to play the role that a ruler does in the visual case. Unless people are cued to “look again” at what initially makes perfect sense, they are not inclined to apply more sophisticated evaluative procedures. (shrink)

We capture the principal models of computation and specification in the literature by a uniform set of transparent mathematical descriptions which—starting from scratch—provide the conceptual basis for a comparative study.1.

Intelligent algorithms together with various machine learning techniques hold a dominant position among major challenges for contemporary value sensitive design. Self-learning capabilities of current AI applications blur the causal link between programmer and computer behavior. This creates a vital challenge for the design, development and implementation of digital technologies nowadays. This paper seeks to provide an account of this challenge. The main question that shapes the current analysis is the following: What conceptual tools can be developed within (...) the value sensitive design school of thought for evaluating machine learning algorithms where the causal relation between designers and the behavior of their computer systems has been eroded? The answer to this question will be provided through two levels of investigation within the value sensitive design methodology. The first level is conceptual. Within the conceptual level, we will introduce the notion of computer intentionality and will show how this term may be used for solving an issue of non-causal relation between designer and computer system. The second level of investigation is technical. At this level the emphasis will be given to machine learning algorithms. (shrink)

During the last years the demand for regionally and culturally harmonised machine design is increasingly on the agenda. The problem of localising products like machine tools instantly poses the question for new procedures that allow including the regional and cultural adaptations into the design processes of machine tool companies. How to transform the general insight into the necessity of culture- and region-adapted technologies and how to embed it into a design procedure comprising applicable (...) class='Hi'>design attributes is the crucial problem addressed. The paper shows in an exemplary way how ambiguous design attributes can eventually be embodied in a prototype design. (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)

Applications of artificial intelligence/machine learning in health care are dynamic and rapidly growing. One strategy for anticipating and addressing ethical challenges related to AI/ml for health care is patient and public involvement in the design of those technologies – often referred to as ‘co-design’. Co-design has a diverse intellectual and practical history, however, and has been conceptualized in many different ways. Moreover, AI/ml introduces challenges to co-design that are often underappreciated. Informed by perspectives from critical (...) data studies and critical digital health studies, we review the research literature on involvement in health care, and involvement in design, and examine the extent to which co-design as commonly conceptualized is capable of addressing the range of normative issues raised by AI/ml for health care. We suggest that AI/ml technologies have amplified and modified existing challenges related to patient and public involvement, and created entirely new challenges. We outline three pitfalls associated with co-design for ethical AI/ml for health care and conclude with suggestions for addressing these practical and conceptual challenges. (shrink)

Value Sensitive Design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and (...) accountability. Second, ML may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: 1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; 2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good; and 3) extending the VSD process to encompass the whole life cycle of an AI technology in order to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app. (shrink)

The presence of intelligence and rationality in Artificial Intelligence and the Internet requires a new context of analysis in which Herbert Simon’s approach to the sciences of the artificial is surpassed in order to grasp the role of information in our contemporary setting. This new framework requires taking into account some relevant aspects. In the historical endeavor of building up AI and the Internet, minds and machines have interacted over the years and in many ways through the interrelation between scientific (...) creativity and technological innovation. Philosophically, minds and machines can have epistemological, methodological and ontological differences, which are based on the distinct configuration of human intelligence and artificial intelligence. Their comparison with rationality and its various forms is particularly relevant. Scientifically, AI and the Internet belong to the sciences of the artificial, because they work on designs that search for specific aims, following selected processes in order to achieve expected results. Technologically, AI and the Internet require the support of information and communication technologies. These have an instrumental role regarding the existence of AI and the Internet. Also ICT shape their diverse forms of configuration over the years.Within this framework, this paper offers a new context of analysis that goes beyond Simon’s and follows four main steps: the interaction between scientific creativity and technological innovation as the philosophico-methodological setting for Artificial Intelligence and the Internet; artificial intelligence and human intelligence as epistemological basis for machines and minds, where the differences between artificial intelligence and human intelligence are made explicit and the analysis of minds and machines is made from the perspective of rationality ; intention and its difference with design of machine learning are considered to distinguish human intelligence from artificial intelligence; and the internal and external aspects of artificial designs in contemporary society are considered through the perspective of rationality, which leads to the transition from intelligence to rationality in the Internet as well as to the historicity of information. (shrink)

This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, (...) here defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: the definition of the problem to be solved, the identification of the data to be collected and set up, and the qualification of the targets to be learned. I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems. (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)

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)

Objective: To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application. Study Design and Setting: This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC. Results: We find that concerns regarding (...) explainability are not limited to MLHC, but rather extend to numerous well-validated treatment interventions as well as to human clinical judgment itself. We examine the role of evidence-based medicine in evaluating unexplainable treatments and technologies, and highlight the analogy between the concept of explainability in MLHC and the related concept of mechanistic reasoning in evidence-based medicine. Conclusion: Ultimately, we conclude that the value of explainability in MLHC is not intrinsic, but is instead instrumental to achieving greater imperatives such as performance and trust. We caution against the uncompromising pursuit of explainability, and advocate instead for the development of robust empirical methods to successfully evaluate increasingly inexplicable algorithmic systems. (shrink)

There is at present a widespread unease about the direction in which our technology is taking us, apparently against our will. Promising advances seem to carry with them unforeseen negative consequences, including damage to the environment and the reduction of work to the trivial mechanical repetition of actions which have no human meaning. However, attempts to design a better, human-centered technology--one that complements rather than rejects human skills--are all too often frustrated by the prevailing belief that "man is a (...) machine," and one, moreover, that compares badly in terms of performance and durability. This contentious and stimulating book offers a new approach, one that refutes four centuries of science based on strictly causal explanations. It shows that man and nature can be viewed as "machines with a purpose," and that the "purpose" can be the advancement of technology to the benefit and not the detriment of the human race and its environment. This fascinating work is accessible to a wide range of readers, scientists and nonspecialists alike. It will interest anyone concerned about the impact of technology and the way it is shaping our world. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Consciousness and Machines:A Commentary Drawing on Japanese PhilosophyS. D. Noam Cook (bio)Viewed from within the great unity of consciousness, thinking is a wave on the surface of a great intuition.Kitarō NishidaIntroductionRecent developments in AI have made the long-standing debate about what computers can and can't do a major public concern. What we understand the properties of such machines to be, and consequently how we design [End Page 305] (...) and deploy them, will increasingly shape how we live and how we understand ourselves. Thus, there is an urgent need for better understanding of these issues.Susan Schneider, in her book Artificial You: AI and the Future of Your Mind, offers an exceptionally careful and insightful examination of key issues entailed in how we understand AI and ourselves. One of her central concerns is how we might test for machine consciousness. Given the variations in design and function among current and projected AIs, Schneider sees no likely one-size-fits-all test. So, she offers a battery of different tests that she believes together will make such testing more reliable. This seems a wise approach, and reflects the conceptual and pragmatic care Schneider takes throughout the book.Similarly, it seems to me that the question of machine consciousness should also include various understandings of consciousness itself. What consciousness is taken to be has varied widely both historically and culturally. No commonly accepted single understanding has emerged, and may never emerge. Just as having a battery of tests might help us better assess possible machine consciousness, having different understandings of consciousness might give us a better understanding of what we are testing for. The two should work in tandem, each gauging and shaping the other.Like the development of computers themselves, the predominant thinking about AI has been shaped, in part, by key presuppositions of a scientific worldview, with its origins in Western thought and culture. Among these presuppositions are materialism, reductionism, and a primary focus on objective behavior. These are reflected in the common concern with observable behavior of physical aspects of the brain in efforts to understand human consciousness, which in turn has become a familiar model for work on machine consciousness. All this is important and valuable work. But, in the spirit of Schneider's cautions, it should not be taken as one-size-fits-all. Accordingly, I offer one view of consciousness from a different perspective.In what follows, I sketch out an understanding of consciousness that draws on Japanese philosophy, particularly that of the highly influential early twentieth-century Japanese philosopher Kitarō Nishida (1870–1945). I then explore how the tests for machine consciousness that Schneider proposes might be viewed from that perspective.Consciousness: A Perspective from Japanese PhilosophyConsciousness was a central concern for Nishida, whose philosophy is deeply informed by Japanese culture and by Zen theory and practice. As one might expect from such a philosopher, Nishida's exploration of consciousness is not concerned with properties of the brain, but with what we might discern through disciplined reflection on the experience of our own consciousness. Ultimately, he views consciousness as an organic whole [End Page 306] of systemically organized interrelationships of activities and sensations within our subjective experience. This may be understood by first considering 'nothingness'.For Nishida, nothingness does not suggest an ultimate and absolute vacuity, as is often understandably assumed. In Nishida's sense, it is a singular "place" that mirrors the multifaceted world of our common experience. In understanding this, I am told by native Japanese speakers that the English word 'nothingness' has an advantage over the Japanese equivalent, 'mu', in that 'nothingness' can usefully be broken down as 'nothing-ness'. This suggests that nothingness is a state of consciousness where what we commonly experience as distinct (self, other, world, blue, ache, joy, etc.) is indistinct, a state of unified experience. This is consciousness without our finding or discriminating in it the myriad "things" of common experience, but rather a "pure experience" of a singular whole. Nishida introduces his concept of "pure experience" as follows:To experience means to know facts just as they are, to know in accordance with facts by completely relinquishing one... (shrink)

How to model meaning processes (semiosis) in artificial semiotic systems? Once all computer simulation becomes tantamount to theoretical simulation, involving epistemological metaphors of world versions, the selection and choice of models will dramatically compromise the nature of all work involving simulation. According to the pragmatic Peircean based approach, semiosis is an interpreter-dependent process that cannot be dissociated from the notion of a situated (and actively distributed) communicational agent. Our approach centers on the consideration of relevant properties and aspects of Peirce’s (...) pragmatic concept of semiotics. Upon developing this approach, we have no pretensions of our being able to present an exhaustive analysis of the differences between Peirce and other theoretical positions. Nevertheless, our contribution will serve to demonstrate how theorists contribute toward revealing certain fundamental ‘semiotic constraints’ that will be of interest and importance. (shrink)