There are plenty of intelligent machines in our world today: digital computers and autonomous robots. At the heart of each of these machines there are automatic formal systems (programs running on a digital computer). Now, if the interpretation of a formal system does not belong to the formal system itself, if the interpretation has to be added, it is worth asking: in the case of these intelligent machines that are massively interspersed in our social interactions, where does the interpretation come (...) from? In this paper, we analyse what we call the invisibility of interpretation. Dealing with various types of formal systems (computers, robots, formalist approaches to Economics), the human source of the interpretation of these systems is sometimes concealed by a formalist restriction. To show how the formalist restriction produces the invisibility of interpretation allows us to underline our responsibility, as human agents, for all this interpretative work—and its importance for us as human beings. (shrink)

In this article I will address the issue of the meaning of human-machine interaction as it is configured today in the light of substantial results achieved in the design and the manufacture of Artificial Intelligence (AI) systems. My starting point is a refined solution for meaningful AI recently suggested by Froese and Taguchi from the perspective of so-called augmented intelligence. Interpreted as a kind of human-machine interaction, augmented intelligence distinguishes itself by the fact that it merges (...) the interacting poles into a new hybrid entity. After having clarified the technical and the methodological background of my proposal, I will discuss a case of study of so-called bio-synthetic augmentation, where hybridisation overlaps with the cyborgisation of the human body. (shrink)

It is highly likely that, to achieve full human–machine symbiosis, truly intelligent cognitive systems—human-like —may have to be developed first. Such systems should not only be capable of performing human-like thinking, reasoning, and problem solving, but also be capable of displaying human-like motivation, emotion, and personality. In this opinion article, I will argue that such systems are indeed possible and needed to achieve true and full symbiosis with humans. A computational cognitive architecture is used in (...) this article to illustrate, in a preliminary way, what can be achieved in this regard. It is shown that Clarion involves complex structures, representations, and mechanisms, and is capable of capturing human cognitive performance as well as human motivation, emotion, personality, and other relevant aspects. It is further argued that the cognitive architecture can enable and facilitate true human–machine symbiosis. (shrink)

The necessity and opportunity for face-to-face contact with other colleagues is being increasingly reduced as a result of factory automation (FA) or office automation (OA). This means that human functions which are a result of human contact and relationships are substituted for by the function of machine systems. This “transfer of relations” from the human “system” to the machine system causes isolation of the individual in the process of work. This chapter considers some reasons for (...) “isolation” with particular reference to the computerisation of production systems. The paper addresses the serious consequences for the environmental situation in Japan and the fabric of Japanese society. (shrink)

Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach “human-level” accuracy in an astounding variety of domains, and even predict human brain activity—raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines fail in strange and “unhumanlike” ways, threatening their status as models of our minds. How can we know when human–machine behavioral differences reflect deep disparities in their (...) underlying capacities, vs. when such failures are only superficial or peripheral? This article draws on a foundational insight from cognitive science—the distinction between performance and competence—to encourage “species-fair” comparisons between humans and machines. The performance/competence distinction urges us to consider whether the failure of a system to behave as ideally hypothesized, or the failure of one creature to behave like another, arises not because the system lacks the relevant knowledge or internal capacities (“competence”), but instead because of superficial constraints on demonstrating that knowledge (“performance”). I argue that this distinction has been neglected by research comparing human and machine behavior, and that it should be essential to any such comparison. Focusing on the domain of image classification, I identify three factors contributing to the species-fairness of human–machine comparisons, extracted from recent work that equates such constraints. Species-fair comparisons level the playing field between natural and artificial intelligence, so that we can separate more superficial differences from those that may be deep and enduring. (shrink)

Technology is our conduit of power. In our modern world, technology is the gatekeeper deciding who shall have and who shall have not. Either technology works for you or you work for technology. It shapes the human race just as much as we shape it. But where is this symbiosis going? Who provides the directions, the intentions, the goals of this human-machine partnership? Such decisions do not derive from the creators of technology who are enmeshed in their (...) individual innovations. They neither come from our social leaders who possess only sufficient technical understanding to react to innovations, not to anticipate or direct their progress. Neither is there evidence of some omnipotent 'invisible hand,' the simple fact is that no one is directing this enterprise. In Mind, Machine and Morality, Peter Hancock asks questions about this insensate progress and has the temerity to suggest some cognate answers. He argues for the unbreakable symbiosis of purpose and process, and examines the dangerous possibilities that emerge when science and purpose meet. Historically, this work is a modern-day child of Bacon's hope for the 'Great Instauration.' However, unlike its forebear, the focus here is on human-machine systems. The emphasis centers on the conception that the active, extensive face of modern philosophy is technology. Whatever we are to become is bound up not only in our biology but critically in our technology also. And to achieve rational progress we need to articulate manifest purpose. This book is one step along the purposive road. (shrink)

Human contact is a key issue in social interactions for autonomous systems since robots are increasingly appearing everywhere, which has led to a higher risk of conflict. Particularly in the real world, collisions between humans and machines may result in catastrophic accidents or damaged goods. In this paper, a novel stop strategy related to autonomous systems is proposed. This control method can eliminate the vibrations produced by a system’s movement by analysing the poles and zeros in the model of (...) autonomous vehicles and goods. Using the pole placement technique, the motion of a system is guaranteed to be more stable, more flexible and smoother. Moreover, several control profiles are employed in the switching mechanism to choose the proper vibration-free effect. The main contributions of this paper are the recommendation of an active stopping planner using different smooth generators from a modelling study, the validation of their physical characteristics and the launching of a switching algorithm based on the socially aware navigation framework of a robot. This theoretical work is based on the virtual environment of MATLAB, and the experiment is implemented in the practical platform of an automated guided vehicle. From these results, it can be seen that the proposed approach is robust, effective and feasible for applications in storehouse management, public transportation or factory manufacturing. (shrink)

In this study, we explore the future of work by examining differences in productivity when teams are composed of only humans or both humans and machine agents. Our objective was to characterize the similarities and differences between human and human–machine teams as they work to coordinate across their specialized roles. This form of research is increasingly important given that machine agents are becoming commonplace in sociotechnical systems and playing a more active role in collaborative work. (...) One particular class of machine agents, bots, is being introduced to these systems to facilitate both taskwork and teamwork. We investigated the association between bots and productivity outcomes in open source software development through an analysis of hundreds of project teams. The presence of bots in teams was associated with higher levels of productivity and higher work centralization in addition to greater amounts of observed communication. The adoption of bots in software teams may have tradeoffs, in that doing so may increase productivity, but could also increase workload. We discuss the theoretical and practical implications of these findings for advancing human–machine teaming research. (shrink)

Artificial agents have become increasingly prevalent in human social life. In light of the diversity of new human–machine interactions, we face renewed questions about the distribution of moral responsibility. Besides positions denying the mere possibility of attributing moral responsibility to artificial systems, recent approaches discuss the circumstances under which artificial agents may qualify as moral agents. This paper revisits the discussion of how responsibility might be distributed between artificial agents and human interaction partners (including producers of (...) artificial agents) and raises the question of whether attributions of responsibility should remain entirely on the human side. While acknowledging a crucial difference between living human beings and artificial systems culminating in an asymmetric feature of human–machine interactions, this paper investigates the extent to which artificial agents may reasonably be attributed a share of moral responsibility. To elaborate on criteria that can justify a distribution of responsibility in certain human–machine interactions, the role of types of criteria (interaction-related criteria and criteria that can be deferred from socially constructed responsibility relationships) is examined. Thereby, the focus will lay on the evaluation of potential criteria referring to the fact that artificial agents surpass in some aspects the capacities of humans. This is contrasted with socially constructed responsibility relationships that do not take these criteria into account. In summary, situations are examined in which it seems plausible that moral responsibility can be distributed between artificial and human agents. (shrink)

Social robots are designed to perform intelligent, emotional, and autonomous behavior in order to establish intimate relationships with humans, for instance, in the context of elderly care. However, the imitation of qualities usually assumed to be necessary for human reciprocal interaction may impact our understanding of social interaction. Against this background, we compare the technical operations based on which social robots imitate human-like behavior with the concepts of emotionality, intelligence, and autonomy as usually attached to humans. In doing (...) so, we aim at making a conceptual contribution to the intuition that the technical imitation of emotional, intelligent, and autonomous behavior differs qualitatively from human forms of emotionality, intelligence, and autonomy. We conclude that the increasing tendency of social robots to imitate human characteristics and the thus increased human tendency of anthropomorphizing technical systems put forth new forms of human–machine interactions (HMI) that call for a critical examination. Thus, the emerging forms of interaction may lead, for instance, to false, reinforced, and perplexing assumptions regarding reciprocity. Therefore, studies are needed that critically examine the potential consequences and psychological effects that may be associated with the use of social robots, especially with regard to vulnerable groups such as children or the elderly. (shrink)

Clinical medicine is an inexact science. In situations of uncertainty, we often ask an experienced colleague for a second opinion. But what if one could effectively call upon the experience of thou...

Based on detailed descriptions of human–machine ensembles, this article explores how humans and machines work together to see specific things and unsee others, and how they come to co-configure one another. For seeing is not an automated function; whether one is a human or a machine, vision is gradually enskilled and mutually co-constituted. The analysis intersects three different ways of human–machine seeing to shed further light on the workings of each one: an airport, where (...) facial recognition algorithms collaborate with border guards to grant passage to particular travellers and not to others; a luggage-scanning system, where potential security threats are assessed by a complex of X-rays and human intro-spection; and a hospital operating room, where human–machinic surgical robots find their way and operate on the insides of human bodies, touching only by seeing. In these examples, human and machine ways of seeing merge together, seeing in particular apparatuses of material, political, organisational, economic and fleshy components. The article analyses the practical work of human–machinic collaboration and explores how the different material and social constituents, not necessarily always working from the same agenda, come to configure what can be seen and sensed and what cannot. (shrink)

With the rise of artificial intelligence (AI) and the desire to ensure that such machines work well with humans, it is essential for AI systems to actively model their human teammates, a capability referred to as Machine Theory of Mind (MToM). In this paper, we introduce the inner loop of human–machine teaming expressed as communication with MToM capability. We present three different approaches to MToM: (1) constructing models of human inference with well-validated psychological theories and (...) empirical measurements; (2) modeling human as a copy of the AI; and (3) incorporating well-documented domain knowledge about human behavior into the above two approaches. We offer a formal language for machine communication and MToM, where each term has a clear mechanistic interpretation. We exemplify the overarching formalism and the specific approaches in two concrete example scenarios. Related work that demonstrates these approaches is highlighted along the way. The formalism, examples, and empirical support provide a holistic picture of the inner loop of human–machine teaming as a foundational building block of collective human–machine intelligence. (shrink)

Interaction between drivers and pedestrians is often facilitated by informal communicative cues, like hand gestures, facial expressions, and eye contact. In the near future, however, when semi- and fully autonomous vehicles are introduced into the traffic system, drivers will gradually assume the role of mere passengers, who are casually engaged in non-driving-related activities and, therefore, unavailable to participate in traffic interaction. In this novel traffic environment, advanced communication interfaces will need to be developed that inform pedestrians of the current state (...) and future behavior of an autonomous vehicle, in order to maximize safety and efficiency for all road users. The aim of the present review is to provide a comprehensive account of empirical work in the field of external human–machine interfaces for autonomous vehicle-to- pedestrian communication. In the great majority of covered studies, participants clearly benefited from the presence of a communication interface when interacting with an autonomous vehicle. Nevertheless, standardized interface evaluation procedures and optimal interface specifications are still lacking. (shrink)

Human decisions are increasingly supported by decision support systems (DSS). Humans are required to remain “on the loop,” by monitoring and approving/rejecting machine recommendations. However, use of DSS can lead to overreliance on machines, reducing human oversight. This paper proposes “reflection machines” (RM) to increase meaningful human control. An RM provides a medical expert not with suggestions for a decision, but with questions that stimulate reflection about decisions. It can refer to data points or suggest counterarguments (...) that are less compatible with the planned decision. RMs think against the proposed decision in order to increase human resistance against automation complacency. Building on preliminary research, this paper will (1) make a case for deriving a set of design requirements for RMs from EU regulations, (2) suggest a way how RMs could support decision-making, (3) describe the possibility of how a prototype of an RM could apply to the medical domain of chronic low back pain, and (4) highlight the importance of exploring an RM’s functionality and the experiences of users working with it. (shrink)

Systems, machines and organisation of forms developed in the Engineering and Manufacturing sectors frequently lay the basis for systems design philosophy at a general level. An analysis of technological change in these sectors reveals that the resultant deskilling is not limited to the shop floor and is now spreading to intellectual work. The impact of ‘machine based systems’ on designers is explored in some detail and suggests the need for alternatives which are based on ‘human centred systems’. Some (...) attempts to design sub-systems are described. (shrink)

If our choices make us who we are, then what does that mean when these choices are made in the human-machine interface? Developing a clear understanding of how human decision making is influenced by automated systems in the environment is critical because, as human-machine interfaces and assistive robotics become even more ubiquitous in everyday life, many daily decisions will be an emergent result of the interactions between the human and the machine – not (...) stemming solely from the human. For example, choices can be influenced by the relative locations and motor costs of the response options, as well as by the timing of the response prompts. In drift diffusion model simulations of response-prompt timing manipulations, we find that it is only relatively equibiased choices that will be successfully influenced by this kind of perturbation. However, with drift diffusion model simulations of motor cost manipulations, we find that even relatively biased choices can still show some influence of the perturbation. We report the results of a two-alternative forced-choice experiment with a computer mouse modified to have a subtle velocity bias in a pre-determined direction for each trial, inducing an increased motor cost to move the cursor away from the pre-designated target direction. With queries that have each been normed in advance to be equibiased in people’s preferences, the participant will often begin their mouse movement before their cognitive choice has been finalized, and the directional bias in the mouse velocity exerts a small but significant influence on their final choice. With queries that are not equibiased, a similar influence is observed. By exploring the synergies that are developed between humans and machines and tracking their temporal dynamics, this work aims to provide insight into our evolving decisions. (shrink)

Acquiring perceptual expertise is slow and effortful. However, untrained novices can accurately make difficult classification decisions by reformulating the task as similarity judgment. Given a query image and a set of reference images, individuals are asked to select the best matching reference. When references are suitably chosen, the procedure yields an implicit classification of the query image. To optimize reference selection, we develop and evaluate a predictive model of similarity-based choice. The model builds on existing psychological literature and accommodates stochastic, (...) dynamic shifts of attention among visual feature dimensions. We perform a series of human experiments with two stimulus types and nine classification tasks to validate the model and to demonstrate the model's potential to boost performance. Our system achieves high accuracy for participants who are naive as to the classification task, even when the classification task switches from trial to trial. (shrink)

The urban traffic environment is characterized by the presence of a highly differentiated pool of users, including vulnerable ones. This makes vehicle automation particularly difficult to implement, as a safe coordination among those users is hard to achieve in such an open scenario. Different strategies have been proposed to address these coordination issues, but all of them have been found to be costly for they negatively affect a range of human values (e.g. safety, democracy, accountability…). In this paper, we (...) claim that the negative value impacts entailed by each of these strategies can be interpreted as lack of what we call Meaningful Human Control over different parts of a sociotechnical system. We argue that Meaningful Human Control theory provides the conceptual tools to reduce those unwanted consequences, and show how “designing for meaningful human control” constitutes a valid strategy to address coordination issues. Furthermore, we showcase a possible application of this framework in a highly dynamic urban scenario, aiming to safeguard important values such as safety, democracy, individual autonomy, and accountability. Our meaningful human control framework offers a perspective on coordination issues that allows to keep human actors in control while minimizing the active, operational role of the drivers. This approach makes ultimately possible to promote a safe and responsible transition to full automation. (shrink)

Although even very advanced artificial systems do not meet the demanding conditions which are required for humans to be a proper participant in a social interaction, we argue that not all human-machine interactions (HMIs) can appropriately be reduced to mere tool-use. By criticizing the far too demanding conditions of standard construals of intentional agency we suggest a minimal approach that ascribes minimal agency to some artificial systems resulting in the proposal of taking minimal joint actions as a case (...) of a social HMI. Analyzing such HMIs, we utilize Dennett’s stance epistemology, and argue that taking either an intentional stance or design stance can be misleading for several reasons, and instead propose to introduce a new stance that is able to capture social HMIs—the AI-stance. (shrink)

This paper uses a proof of Gödels theorem, implemented on a computer, to explore how a person or a computer can examine such a proof, understand it, and evaluate its validity. It is argued that, in order to recognize it (1) as Gödel's theorem, and (2) as a proof that there is an undecidable statement in the language of PM, a person must possess a suitable semantics. As our analysis reveals no differences between the processes required by people and machines (...) to understand Gödel's theorem and manipulate it symbolically, an effective way to characterize this semantics is to model the human cognitive system as a Turing Machine with sensory inputs. La logistique n'est plus stérile: elle engendre la contradicion! – Henri Poincaré ‘Les mathematiques et la logique’. (shrink)

Generative art theory involves any artistic practice where an artist uses external systems (rules, algorithms, organic processes) to complete an artwork. Commonly utilized in video game design, gen...

In recent years, we have experienced rapid development of advanced technology, machine learning, and artificial intelligence (AI), intended to interact with and augment the abilities of humans in practically every area of life. With the rapid growth of new capabilities, such as those enabled by generative AI (e.g., ChatGPT), AI is increasingly at the center of human communication and collaboration, resulting in a growing recognition of the need to understand how humans and AI can integrate their inputs in (...) collaborative teams. However, there are many unanswered questions regarding how human–AI collective intelligence will emerge and what the barriers might be. Truly integrated collaboration between humans and intelligent agents may result in a different way of working that looks nothing like what we know now, and it is important to keep the essential goal of human societal well-being and prosperity a priority. In this special issue, we begin to scope out the underpinnings of a socio-cognitive architecture for Collective HUman-MAchine INtelligence (COHUMAIN), which is the study of the capability of an integrated human and machine (i.e., intelligent technology) system to achieve goals in a wide range of environments. This topic consists of nine papers including a description of the conceptual foundation for a socio-cognitive architecture for COHUMAIN, empirical tests of some aspects of this architecture, research on proposed representations of intelligent agents that can jointly interact with humans, empirical tests of human–human and human–machine interactions, and philosophical and ethical issues to consider as we develop these systems. (shrink)

Ethical dissonance arises from conflicts between beliefs or behaviors and affects ethical factors such as normality or conformity. This paper proposes a weak signal-oriented framework to investigate ethical dissonance from experiences linked to human–machine interactions. It is based on a systems engineering principle called human-systems inclusion, which considers any experience feedback of weak signals as beneficial to learn. The framework studies weak signal-based scenarios from testimonies of individual experiences and these scenarios are assessed by other people. For (...) this purpose, the framework proposes several databases as sources of weak signals, formalization tools of experience feedback of weak signals, models of references of conformity, ethical factors, and a list of examples of ethical dissonance. It also includes sequential steps to make the latter credible regarding the results from the experimental protocols. The framework was used to investigate ethical dissonance by analyzing experiences pertaining to achieving inclusive mobility. The first example focuses on ethical dissonance in terms of hindrance that goes against autonomous mobility due to a misunderstanding of how the system functions in terms of negative and positive emotions. Two other examples present possible ethical dissonance when the use of safety systems such as car driver-assistance systems, may create danger. Investigating ethical dissonance can then help system-inclusive design or evaluation processes by taking into account scenarios from weak signal-based experiences and making them credible. (shrink)

Rational social theorists have failed to confirm that observations of social reality equal social reality. Yet they argue that teams, organizations and social systems should minimize interdependence and competition, echoed by social psychologists to make data iid. But the evidence indicates that competitive teams maximize interdependence; self-reports of social reality correlate poorly with social behavior; and only competition measures interdependent social states. Rational expectations aside, we report progress towards a science of interdependence for human–machine teams. Our model of (...) interdependence works like an uncertainty principle in the sense that tradeoffs arise from the uncertainty caused by measuring interdependent actors in orthogonal roles; e.g., in the tradeoff between teams and individuals, teams are more productive but more opaque. Previously, we described interdependence as bistable stories of social reality; the motivation to reject alternative interpretations, increasing uncertainty and errors; and the inability to factor social states. Now we explore education as a surrogate for intelligence in teams. We hypothesized that teams rely on the education of its members to produce more patents. We found that the average schooling in a nation is significantly related to its total patents produced. (shrink)

Machine learning systems are making considerable inroads in society owing to their ability to recognize and predict patterns. However, the decision-making logic of some widely used machine learning models, such as deep neural networks, is characterized by opacity, thereby rendering them exceedingly difficult for humans to understand and explain and, as a result, potentially risky to use. Considering the importance of addressing this opacity, this paper calls for research that studies empirically and theoretically how machine learning experts (...) and users seek to attain machine learning explainability. Focusing on automated trading, we take steps in this direction by analyzing a trading firm’s quest for explaining its deep neural network system’s actionable predictions. We demonstrate that this explainability effort involves a particular form of human–machine interaction that contains both anthropomorphic and technomorphic elements. We discuss this attempt to attain machine learning explainability in light of reflections on cross-species companionship and consider it an example of human–machine companionship. (shrink)

Emergent digital technologies provide cultural heritage spaces with the opportunity to reassess their current user journey. An immersive user experience can be developed that is innovative, dynamic, and customised for each attendee. Museums have already begun to move towards interactive exhibitions utilising Artificial Intelligence (AI) and the Internet of Things (IOT), and more recently, the use of Virtual Reality (VR) and Augmented Reality (AR) has become more common in cultural heritage spaces to present items of historical significance. VR concentrates on (...) the provision of full immersion within a digitised environment utilising a headset, whilst AR focuses on the inclusion of digitised content within the existing physical environment that can be accessed through a medium such as a mobile phone application. Machine learning techniques such as a recommender system can support an immersive user journey by issuing personalised recommendations regarding a user’s preferred future content based on their previous activity. An ethical approach is necessary to take the precautions required to protect the welfare of human participants and eliminate any aspect of stereotyping or biased behaviour. This paper sets out a human-centred manifesto intended to provide guidance when inducing smart digital immersion in cultural heritage spaces. A review of existing digital cultural heritage projects was conducted to determine their adherence to the manifesto with the findings indicating that Education was a primary focus across all projects and that Personalisation, Respect and Empathy, and Support were also highly valued. Additionally, the findings indicated that there were areas with room for improvement such as Fairness to ensure that a well-balanced human-centred system is implemented. (shrink)

Rapid developments in Artificial Intelligence are leading to an increasing human reliance on machine decision making. Even in collaborative efforts with Decision Support Systems (DSSs), where a human expert is expected to make the final decisions, it can be hard to keep the expert actively involved throughout the decision process. DSSs suggest their own solutions and thus invite passive decision making. To keep humans actively ‘on’ the decision-making loop and counter overreliance on machines, we propose a ‘reflection (...) machine’ (RM). This system asks users questions about their decision strategy and thereby prompts them to evaluate their own decisions critically. We discuss what forms RMs can take and present a proof-of-concept implementation of a RM that can produce feedback on users’ decisions in the medical and law domains. We show that the prototype requires very little domain knowledge to create reasonably intelligent critiquing questions. With this prototype, we demonstrate the technical feasibility to develop RMs and hope to pave the way for future research into their effectiveness and value. (shrink)

In the modern world, many important tasks have become too complex for a single unaided individual to manage. Teams conduct some safety-critical tasks to improve task performance and minimize the risk of error. These teams have traditionally consisted of human operators, yet, nowadays, artificial intelligence and machine systems are incorporated into team environments to improve performance and capacity. We used a computerized task modeled after a classic arcade game to investigate the performance of human–machine and (...) class='Hi'>human–human teams. We manipulated the group conditions between team members; sometimes, they were instructed to collaborate, compete, or work separately. We evaluated players' performance in the main task (gameplay) and, in post hoc analyses, participant behavioral patterns to inform group strategies. We compared game performance between team types (human–human vs. human–machine) and group conditions (competitive, collaborative, independent). Adapting workload capacity analysis to human–machine teams, we found performance under both team types and all group conditions suffered a performance efficiency cost. However, we observed a reduced cost in collaborative over competitive teams within human–human pairings, but this effect was diminished when playing with a machine partner. The implications of workload capacity analysis as a powerful tool for human–machine team performance measurement are discussed. (shrink)

It is held that the quality of the working environment afforded to an individual critically affects the health and well-being of that individual. This has consequences for both the quality of work which that individual can actually perform, and for the quality of the society in which that individual has a place. Conceptions of a fit working environment have led to the idea of a human-centred system, and this idea is applicable to the area of knowledge-based systems (KBS). A (...) system structure is discussed, which permits a human-centred approach to be adopted towards KBS design. It is held that the performance of such a system will generally exceed that of unaided KBS operation. (shrink)

It is the height of banality to observe that people, not bullets, fight kinetic wars. The machinery of kinetic warfare is obviously relevant to the conduct of each particular act of warfare, but the reasons for, and meanings of, those acts depend critically on the fact that they are done by humans. Any attempt to understand warfare—its causes, strategies, legitimacy, dynamics, and resolutions—must incorporate humans as an intrinsic part, both descriptively and normatively. Humans from general staff to “boots on the (...) ground” play key roles in all aspects of kinetic warfare, and the literature about it reflects this focus. 2 In contrast, many discussions of cyberwarfare and cyber-conflict focus principally on the technical aspects of machines, systems, and data, 3 and human agents are included only as collateral effects, or as loci of moral responsibility. (shrink)

Therapeutic strategies capable of inducing and enhancing prosthesis embodiment are a key point for better adaptation to and acceptance of prosthetic limbs. In this study, we developed a training protocol using an EMG-based human-machine interface that was applied in the preprosthetic rehabilitation phase of people with amputation. This is a case series with the objective of evaluating the induction and enhancement of the embodiment of a virtual prosthesis. Six men and a woman with unilateral transfemoral traumatic amputation without (...) previous use of prostheses participated in the study. Participants performed a training protocol with the EMG-based HMI, composed of six sessions held twice a week, each lasting 30 mins. This system consisted of myoelectric control of the movements of a virtual prosthesis immersed in a 3D virtual environment. Additionally, vibrotactile stimuli were provided on the participant’s back corresponding to the movements performed. Embodiment was investigated from the following set of measurements: skin conductance response, crossmodal congruency effect, ability to control the virtual prosthesis, and reports before and after the training. The increase in the skin conductance response in conditions where the virtual prosthesis was threatened, recalibration of the peripersonal space perception identified by the crossmodal congruency effect, ability to control the virtual prosthesis, and participant reports consistently showed the induction and enhancement of virtual prosthesis embodiment. Therefore, this protocol using EMG-based HMI was shown to be a viable option to achieve and enhance the embodiment of a virtual prosthetic limb. (shrink)

During the 1970s and 1980s, a team of Automated Theorem Proving researchers at the Argonne National Laboratory near Chicago developed the Automated Reasoning Assistant, or AURA, to assist human users in the search for mathematical proofs. The resulting hybrid humans+AURA system developed the capacity to make novel contributions to pure mathematics by very untraditional means. This essay traces how these unconventional contributions were made and made possible through negotiations between the humans and the AURA at Argonne and the transformation (...) in mathematical intuition they produced. At play in these negotiations were experimental practices, nonhumans, and nonmathematical modes of knowing. This story invites an earnest engagement between historians of mathematics and scholars in the history of science and science studies interested in experimental practice, material culture, and the roles of nonhumans in knowledge making. (shrink)

As we increasingly delegate decision-making to algorithms, whether directly or indirectly, important questions emerge in circumstances where those decisions have direct consequences for individual rights and personal opportunities, as well as for the collective good. A key problem for policymakers is that the social implications of these new methods can only be grasped if there is an adequate comprehension of their general technical underpinnings. The discussion here focuses primarily on the case of enforcement decisions in the criminal justice system, but (...) draws on similar situations emerging from other algorithms utilised in controlling access to opportunities, to explain how machine learning works and, as a result, how decisions are made by modern intelligent algorithms or 'classifiers'. It examines the key aspects of the performance of classifiers, including how classifiers learn, the fact that they operate on the basis of correlation rather than causation, and that the term 'bias' in machine learning has a different meaning to common usage. An example of a real world 'classifier', the Harm Assessment Risk Tool (HART), is examined, through identification of its technical features: the classification method, the training data and the test data, the features and the labels, validation and performance measures. Four normative benchmarks are then considered by reference to HART: (a) prediction accuracy (b) fairness and equality before the law (c) transparency and accountability (d) informational privacy and freedom of expression, in order to demonstrate how its technical features have important normative dimensions that bear directly on the extent to which the system can be regarded as a viable and legitimate support for, or even alternative to, existing human decision-makers. (shrink)

Intelligence is not a property unique to the human brain; rather it represents a spectrum of phenomena. An understanding of the evolution of intelligence makes it clear that the evolution of machine intelligence has no theoretical limits — unlike the evolution of the human brain. Machine intelligence will outpace human intelligence and very likely will do so during the lifetime of our children. The mix of advanced machine intelligence with human individual and communal (...) intelligence will create an evolutionary discontinuity as profound as the origin of life. It will presage the end of the human species as we know it. The question, in the author's view, is not whether this will happen, but when, and what should be our response. (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)

Within the ethical debate on Machine Learning-driven decision support systems (ML_CDSS), notions such as “human in the loop” or “meaningful human control” are often cited as being necessary for ethical legitimacy. In addition, ethical principles usually serve as the major point of reference in ethical guidance documents, stating that conflicts between principles need to be weighed and balanced against each other. Starting from a neo-Kantian viewpoint inspired by Onora O'Neill, this article makes a concrete suggestion of how (...) to interpret the role of the “human in the loop” and to overcome the perspective of rivaling ethical principles in the evaluation of AI in health care. We argue that patients should be perceived as “fellow workers” and epistemic partners in the interpretation of ML_CDSS outputs. We further highlight that a meaningful process of integrating (rather than weighing and balancing) ethical principles is most appropriate in the evaluation of medical AI. (shrink)

Human-extended machine cognition is a specific form of artificial intelligence in which the casually-active physical vehicles of machine-based cognitive states and processes include one or more human agents. Human-extended machine cognition is thus a specific form of extended cognition that sees human agents as constituent parts of the physical fabric that realizes machine-based cognitive capabilities. This idea is important, not just because of its impact on current philosophical debates about the extended character (...) of human cognition, but also because it helps to focus scientific attention on the potential role of the human social environment in realizing novel forms of artificial intelligence. The present paper provides an overview of human-extended machine cognition and situates the concept within the broader theoretical framework of active externalism. The paper additionally shows how the concept of human-extended machine cognition can be applied to existing forms of human–machine interaction, especially those that occur in the context of the contemporary Internet and Web. (shrink)

The human blood coagulation system comprises a series of linked glycoproteins that upon activation induce the generation of downstream enzymes ultimately forming fibrin. This process is primarily important to arrest bleeding (hemostasis). Hemostasis is a typical example of a molecular machine, where the assembly of substrates, enzymes, protein cofactors and calcium ions on a phospholipid surface markedly accelerates the rate of coagulation. Excess, pathological, coagulation activity occurs in “thrombosis”, the formation of an intravascular clot, which in the most (...) dramatic form precipitates in the microvasculature as disseminated intravascular coagulation. Thrombosis occurs according to a biochemical machine model in the case of atherothrombosis on a ruptured atherosclerotic plaque, but may develop at a slower rate in venous thrombosis, illustrating that the coagulation machinery can act at different velocities. The separate coagulation enzymes are also important in other biological processes, including inflammation for which the rapid conversion of one coagulation factor by the other is not a prerequisite. The latter role of coagulation enzymes may be related to the old and probably maintained function of the coagulation machine in innate immunity. BioEssays 25:1220–1228, 2003. © 2003 Wiley Perioicals, Inc. (shrink)

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

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram and functional near-infrared spectroscopy are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to (...) enhance brain–computer interface performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go. (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)

The vision of the new generation of office systems is based on the hypothesis that an automatic support system is all the more useful and acceptable, the more systems behaviour and performance are in accordance with features ofhuman behaviour. Consequently recent development activities are influenced by the paradigm of the computer as man's “cooperative assistant”. The metaphors ofassistance andcooperation illustrate some major requirements to be met by new office systems. Cooperative office systems will raise a set of new questions about (...) the future of human work, human-machine interaction, the forms of individual control of work, the scope of action and the development of competence in the frame of AI-supported cooperative work, the relative benefits of different types of organizations, etc. With increasing autonomy of the computer in task accomplishment, research should also be concerned with the question of the limits of such a development. In AI-research and development there is much discussion of the intended performance of that new technology. Perhaps this will provide insights into, how these new machines should support numerous aspects of individual or cooperative work. But we find fewer ideas about the future ofhuman work. What will the role of thehuman actor be in future AI-supported cooperative work? What kind of work do we want to support with AI-machines? In the following article I will try to identify some questions for further sociological research activities. I will base my considerations on some theoretical aspects ofunderstanding andmeaning. Regarding identical and non-identical aspects in communication behaviour of human and machines, I will focus on some questions to be investigated. Finally some methodological problems of sociological research in the field of Artificial Intelligence will be discussed, especially the so-called “Time-Dilemma” of sociological research in technology. (shrink)

As human–machine teams are being considered for a variety of mixed-initiative tasks, detecting and being responsive to human cognitive states, in particular systematic cognitive states, is among the most critical capabilities for artificial systems to ensure smooth interactions with humans and high overall team performance. Various human physiological parameters, such as heart rate, respiration rate, blood pressure, and skin conductance, as well as brain activity inferred from functional near-infrared spectroscopy or electroencephalogram, have been linked to different (...) systemic cognitive states, such as workload, distraction, or mind–wandering among others. Whether these multimodal signals are indeed sufficient to isolate such cognitive states across individuals performing tasks or whether additional contextual information (e.g., about the task state or the task environment) is required for making appropriate inferences remains an important open problem.In this paper, we introduce an experimental and machine learning framework for investigating these questions and focus specifically on using physiological and neurophysiological measurements to learn classifiers associated with systemic cognitive states like cognitive load, distraction, sense of urgency, mind wandering, and interference. Specifically, we describe a multitasking interactive experimental setting used to obtain a comprehensive multimodal data set which provided the foundation for a first evaluation of various standard state-of-the-art machine learning techniques with respect to their effectiveness in inferring systemic cognitive states. While the classification success of these standard methods based on just the physiological and neurophysiological signals across subjects was modest, which is to be expected given the complexity of the classification problem and the possibility that higher accuracy rates might not in general be achievable, the results nevertheless can serve as a baseline for evaluating future efforts to improve classification, especially methods that take contextual aspects such as task and environmental states into account. (shrink)

Autonomy has been central to moral and political philosophy for millenia, and has been positioned as a critical aspect of both justice and wellbeing. Research in psychology supports this position, providing empirical evidence that autonomy is critical to motivation, personal growth and psychological wellness. Responsible AI will require an understanding of, and ability to effectively design for, human autonomy (rather than just machine autonomy) if it is to genuinely benefit humanity. Yet the effects on human autonomy of (...) digital experiences are neither straightforward nor consistent, and are complicated by commercial interests and tensions around compulsive overuse. This multi-layered reality requires an analysis that is itself multidimensional and that takes into account human experience at various levels of resolution. We borrow from HCI and psychological research to apply a model (“METUX”) that identifies six distinct spheres of technology experience. We demonstrate the value of the model for understanding human autonomy in a technology ethics context at multiple levels by applying it to the real-world case study of an AI-enhanced video recommender system. In the process we argue for the following three claims: 1) There are autonomy-related consequences to algorithms representing the interests of third parties, and they are not impartial and rational extensions of the self, as is often perceived; 2) Designing for autonomy is an ethical imperative critical to the future design of responsible AI; and 3) Autonomy-support must be analysed from at least six spheres of experience in order to approriately capture contradictory and downstream effects. (shrink)

Consciousness is widely perceived as one of the most fundamental, interesting and difficult problems of our time. However, we still know next to nothing about the relationship between consciousness and the brain and we can only speculate about the consciousness of animals and machines. Human and Machine Consciousness presents a new foundation for the scientific study of consciousness. It sets out a bold interpretation of consciousness that neutralizes the philosophical problems and explains how we can make scientific predictions (...) about the consciousness of animals, brain-damaged patients and machines. Gamez interprets the scientific study of consciousness as a search for mathematical theories that map between measurements of consciousness and measurements of the physical world. We can use artificial intelligence to discover these theories and they could make accurate predictions about the consciousness of humans, animals and artificial systems. Human and Machine Consciousness also provides original insights into unusual conscious experiences, such as hallucinations, religious experiences and out-of-body states, and demonstrates how 'designer' states of consciousness could be created in the future. Gamez explains difficult concepts in a clear way that closely engages with scientific research. His punchy, concise prose is packed with vivid examples, making it suitable for the educated general reader as well as philosophers and scientists. Problems are brought to life in colourful illustrations and a helpful summary is given at the end of each chapter. The endnotes provide detailed discussions of individual points and full references to the scientific and philosophical literature."--Publisher's website. (shrink)

We are living in a time of meta-organics and post-biology, where we perceive everything in our world as customizable and changeable. Modelling biology within a technological context allows us to investigate GEO-volutionary alternatives/alterations to our original natural systems, where augmentation and transmutation become standards in search of overall betterment (Genetically Engineered Organics). Our expectations for technology exceeds ubiquitous access and functional perfection and enters the world of technoetics, where our present hyper-functional, immersively multi-apped, borderline-prosthetic, global village devices fail to satiate (...) our desires to be synaptically surpassed. Most real-time interactive behavioural systems in art utilize two and three dimensions. Artificial neural networks and prediction (Latin: from pr- before plus dicere to say) systems give rise to experimentation within the third-plus-one (3+1) dimension of the spacetime continuum. Spacetime in accordance with string theory requires the where and when to describe something in existence, rather than mere points in space. It explains the workings of the universe from both super-galactic and subatomic levels. Physical cosmology studies the motions of the celestial bodies in relation to the first cause (ie. primum movens) or the source of all-being. In book 12 of Metaphysics, Aristotle spoke of something which moves without (itself) being moved (by anything), and referred to being as motion. In quantum mechanics, a particle is described by a wave. Physiognomy is an evolving area of psychological research that describes a direct correlation between human gesture (or movement) and thought. Early twentieth-century Persian philosopher Abdul-Baha mentioned that the reality of man is his thought, not his material body. Prediction systems apply models of the mind, or artificial neural networks and machine learning strategies, to use the recorded past with the present to anticipate the future. This article investigates existing human and artificial psychic systems in hopes of identifying tools necessary in building a gesture prediction system. The relationship of intent to gesture and whether intent can be perceived without the evidence of gesture are two factors that must be tested. (shrink)

In my opinion, it is only in the framework of a research dealing with abductive cognition that we can analyze important cognitive aspects of human and machine capacities. From the point of view of human capacities the phenomenological concept of anticipation, which is related to the problem of the spontaneous generation of spatiality and its three-dimensionality, will be central. I will describe that anticipations can be seen as types of visual and manipulative abduction and also fruitful to (...) illustrate, in the case of human and machine capacities, the respective role of two kinds of strategic reasoning: locked and unlocked abductive strategies, which characterize the basic cognitive pro- cedure of “reading ahead”. The specificity of these contrasting inferential strategies is also related to their potentiality in producing different kinds of hypothetical outcomes, which in turn represent dissimilar levels of knowledge creativity. This diversity is also fundamental to depict the special character, the kind of creativity, and the limits of current computational deep learning AI systems, such as AlphaGo, which realize abductive cognitive processes. (shrink)

This paper presents a new method to recognize human activities based on weighted classification for the features extracted by human body. Towards this end, new features depend on weight taken from image or video used in proposed descriptor. Human pose plays an important role in extracted features; then these features are used as the weight input with classifier. We use machine learning during two steps of training and testing images of standard dataset that can be used (...) during benchmarking the system. Unlike previous methods that need size or length of shapes mainly to represent the cues when machine learning is used to recognize human activities, accurate experimental results coming from appropriate segments of the human body proved the worthiness of proposed method. Twelve activities are used in challenging of availability comparison with dataset to demonstrate our method. The results show that we achieved 87.3% in training set, while in testing set, we achieved 94% in terms of precision. (shrink)

Legislation and ethical guidelines around the globe call for effective human oversight of AI-based systems in high-risk contexts – that is oversight that reliably reduces the risks otherwise associated with the use of AI-based systems. Such risks may relate to the imperfect accuracy of systems (e.g., inaccurate classifications) or to ethical concerns (e.g., unfairness of outputs). Given the significant role that human oversight is expected to play in the operation of AI-based systems, it is crucial to better understand (...) the conditions for effective human oversight. We argue that the reliable detection of errors (as an umbrella term for inaccuracies and unfairness) is crucial for effective human oversight. We then propose that Signal Detection Theory (SDT) offers a promising framework for better understanding what affects people’s sensitivity (i.e., how well they are able to detect errors) and response bias (i.e., the tendency to report errors given a perceived evidence of an error) in detecting errors. Whereas an SDT perspective on the detection of inaccuracies is straightforward, we demonstrate its broader applicability by detailing the specifics for an SDT perspective on unfairness detection, including the need to choose a standard for (un)fairness. Additionally, we illustrate that an SDT perspective helps to better understand the conditions for effective error detection by showing examples of task-, system-, and person-related factors that may affect the sensitivity and response bias of humans tasked with detecting unfairness associated with the use of AI-based systems. Finally, we discuss future research directions for an SDT perspective on error detection. (shrink)