Due to the ongoing advancements in technology, socio-technical collaboration has become increasingly prevalent. This poses challenges in terms of governance and accountability, as well as issues in various other fields. Therefore, it is crucial to familiarize decision-makers and researchers with the core of human–machine collaboration. This study introduces a taxonomy that enables identification of the very nature of human–machine interaction. A literature review has revealed that automation and technical autonomy are main parameters for describing and understanding (...) such interaction. Both aspects must be carefully evaluated, as their increase has potentially far-reaching consequences. Hence, these two concepts comprise the taxonomy’s axes. Five levels of automation and five levels of technical autonomy are introduced below, based on the assumption that both automation and autonomy are gradual. The levels of automation were developed from existing approaches; those of autonomy were carefully derived from a review of the literature. The taxonomy’s use is also explained, as are its limitations and avenues for further research. (shrink)

Artificial intelligence is poised to eliminate millions of jobs, from finance to truck driving. But artisanal products are valued precisely because of their human origins, and thus have some inherent “immunity” from AI job loss. At the same time, artisanal labor, combined with technology, could potentially help to democratize the economy, allowing independent, small-scale businesses to flourish. Could AI, robotics and related automation technologies enhance the economic viability and environmental sustainability of these beloved crafting professions, perhaps even expanding their niche (...) to replace some job loss in other sectors? In this paper, we compare the problems created by the current mass production economy and potential solutions from an artisanal economy. In doing so, the paper details the possibilities of utilizing AI to support hybrid forms of human–machine production at the microscale; localized and sustainable value chains at the mesoscale; and networks of these localized and sustainable producers at the macroscale. In short, a wide range of automation technologies are potentially available for facilitating and empowering an artisanal economy. Ultimately, it is our hope that this paper will facilitate a discussion on a future vision for more “generative” economic forms in which labor value, ecological value and social value can circulate without extraction or alienation. (shrink)

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)

Many ethicists writing about automated systems attribute agency to these systems. Not only that; they seemingly attribute an autonomous or independent form of agency to these machines. This leads some ethicists to worry about responsibility-gaps and retribution-gaps in cases where automated systems harm or kill human beings. In this paper, I consider what sorts of agency it makes sense to attribute to most current forms of automated systems, in particular automated cars and military robots. I argue that whereas it indeed (...) makes sense to attribute different forms of fairly sophisticated agency to these machines, we ought not to regard them as acting on their own, independently of any human beings. Rather, the right way to understand the agency exercised by these machines is in terms of human–robot collaborations, where the humans involved initiate, supervise, and manage the agency of their robotic collaborators. This means, I argue, that there is much less room for justified worries about responsibility-gaps and retribution-gaps than many ethicists think. (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)

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)

This paper analyzes creative practice including virtual music composition by a human and sets of computer programs, improvisation of music and dance in human-robot ensembles, and drawings produced by a human and a robotic arm. In all of these examples, the paper argues that creativity arises from a process of human-robot collaboration. Human influences on the machines involved exist at many levels, from initial creation and programming, via processes of reprogramming and setup of underlying data and parameters, to engagement (...) throughout the process of creative production. The decision to value a machine as a creative other is supported most strongly when collaborating with the machine directly, while witnessing the creative team at work, as opposed simply to seeing the result, is more likely to bring an audience to a similar understanding. The creativity of the human-machine collaborations analyzed in this paper relies on close interaction, within which there is a continual recognition of the otherness of the machine and its nonhuman abilities. Such relations can be theorized by extending Emmanuel Levinas’ conception of the face-to-face encounter within which self and other are brought into proximity, but the alterity of the other is nonetheless retained. The paper’s analysis of creative interactions between humans and robots supports the idea that machines need not be regarded as challenging human artistic practice, but rather enable new ways for creativity to arise through human-machine collaborations within which human and nonhuman creative abilities are combined. (shrink)

Ethics review processes are frequently perceived as extending from codes and protocols rooted in biomedical disciplines. As a result, many researchers in the humanities and social sciences (HASS) find these processes to be misaligned, if not outrightly obstructive to their research. This leads some scholars to advocate against HASS participation in institutional review processes as they currently stand, or in their entirety. While ethics review processes can present a challenge to HASS researchers, these are not insurmountable and, in fact, present (...) opportunities for ethics review boards (ERBs) to mediate their practices to better attend to the concerns of the HASS disciplines. By highlighting the potential value of the ethics review process in recognising the nuances and specificity across different forms of research, this article explores the generative possibilities of greater collaboration between HASS researchers and ERBs. Remaining cognisant of the epistemic and methodological differences that mark different disciplinary formations in turn will benefit the ethical conduct of all researchers. (shrink)

In an increasingly complex military operating environment, next generation wargaming platforms can reduce risk, decrease operating costs, and improve overall outcomes. Novel Artificial Intelligence enabled wargaming approaches, based on software platforms with multimodal interaction and visualization capacity, are essential to provide the decision-making flexibility and adaptability required to meet current and emerging realities of warfighting. We highlight three areas of development for future warfighter-machine interfaces: AI-directed decisional guidance, computationally informed decision-making, and realistic representations of decision spaces. Progress in these areas (...) will enable development of effective human-AI collaborative decision-making, to meet the increasing scale and complexity of today’s battlespace. (shrink)

In this information society of today, it is often argued that it is necessary to create a new way of human-machine interaction. In this paper, an agent with social response capabilities has been developed to achieve this goal. There are two kinds of information that is exchanged by two entities: objective and functional information and subjective information. Traditional interactive systems have been designed to handle the former kind of information. In contrast, in this study social agents handling the latter type (...) of information are presented. The current study focuses on sociality of the agent from the view point of Media Equation theory. This article discusses the definition, importance, and benefits of social intelligence as agent technology and argues that social intelligence has a potential to enhance the user's perception of the system, which in turn can lead to improvements of the system's performance. In order to implement social intelligence in the agent, a mind model has been developed to render affective expressions and personality of the agent. The mind model has been implemented in a human-machine collaborative learning system. One differentiating feature of the collaborative learning system is that it has an agent that performs as a co-learner with which the user interacts during the learning session. The mind model controls the social behaviors of the agent, thus making it possible for the user to have more social interactions with the agent. The experiment with the system suggested that a greater degree of learning was achieved when the students worked with the co-learner agent and that the co-learner agent with the mind model that expressed emotions resulted in a more positive attitude toward the system. (shrink)

As we move towards a world that is using machine learning and nascent artificial intelligence to analyse and, in many ways, guide most aspects of our lives, new forms of heterogeneous collaborative teams that include human/intelligent machine agents will become not just possible, but an inevitable part of our shared world. The conscious participation of the arts in the conversation about, and development and implementation of, these new collaborative possibilities is crucial, as the arts serve as our best lens through (...) which we can explore the full spectrum of potential possibilities and futures. Through the complex relationships formed through heterogeneous human/intelligent agent collaboration, we are given the opportunity to re-examine some of our assumptions and suppositions of teamwork and forms of relating and thinking within a group environment. (shrink)

Bioethics, Volume 36, Issue 2, Page 134-142, February 2022.

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)

Abstract Philosophical discussion of Alan Turing’s writings on intelligence has mostly revolved around a single point made in a paper published in the journal Mind in 1950. This is unfortunate, for Turing’s reflections on machine (artificial) intelligence, human intelligence, and the relation between them were more extensive and sophisticated. They are seen to be extremely well-considered and sound in retrospect. Recently, IBM developed a question-answering computer (Watson) that could compete against humans on the game show Jeopardy! There are hopes it (...) can be adapted to other contexts besides that game show, in the role of a collaborator of, rather than a competitor to, humans. Another, different, research project --- an artificial intelligence program put into operation in 2010 --- is the machine learning program NELL (Never Ending Language Learning), which continuously ‘learns’ by ‘reading’ massive amounts of material on millions of web pages. Both of these recent endeavors in artificial intelligence rely to some extent on the integration of human guidance and feedback at various points in the machine’s learning process. In this paper, I examine Turing’s remarks on the development of intelligence used in various kinds of search, in light of the experience gained to date on these projects. (shrink)

Deep learning models are powerful tools for representing the complex learning processes and decision-making strategies used by humans. Such neural network models make fewer assumptions about the underlying mechanisms thus providing experimental flexibility in terms of applicability. However, this comes at the cost of involving a larger number of parameters requiring significantly more data for effective learning. This presents practical challenges given that most cognitive experiments involve relatively small numbers of subjects. Laboratory collaborations are a natural way to increase overall (...) dataset size. However, data sharing barriers between laboratories as necessitated by data protection regulations encourage the search for alternative methods to enable collaborative data science. Distributed learning, especially federated learning, which supports the preservation of data privacy, is a promising method for addressing this issue. To verify the reliability and feasibility of applying FL to train neural networks models used in the characterization of decision making, we conducted experiments on a real-world, many-labs data pool including experiment data-sets from ten independent studies. The performance of single models trained on single laboratory data-sets was poor. This unsurprising finding supports the need for laboratory collaboration to train more reliable models. To that end we evaluated four collaborative approaches. The first approach represents conventional centralized learning and is the optimal approach but requires complete sharing of data which we wish to avoid. The results however establish a benchmark for the other three approaches, federated learning, incremental learning, and cyclic incremental learning. We evaluate these approaches in terms of prediction accuracy and capacity to characterize human decision-making strategies. The FL-based model achieves performance most comparable to that of the CL-based model. This indicates that FL has value in scaling data science methods to data collected in computational modeling contexts when data sharing is not convenient, practical or permissible. (shrink)

To realize a successful and collaborative interaction between human and robots remains a big challenge. Emotional reactions of the user provide crucial information for a successful interaction. These reactions carry key factors to prevent errors and fatal bidirectional misunderstanding. In cases where human–machine interaction does not proceed as expected, negative emotions, like frustration, can arise. Therefore, it is important to identify frustration in a human–machine interaction and to investigate its impact on other influencing factors such as dominance, sense (...) of control and task performance. This paper presents a study that investigates a close cooperative work situation between human and robot, and explore the influence frustration has on the interaction. The task for the participants was to hand over colored balls to two different robot systems. The robot systems had to throw the balls into appropriate baskets. The coordination between human and robot was controlled by various gestures and words by means of trial and error. Participants were divided into two groups, a frustration- and a no frustration- group. Frustration was induced by the behavior of the robotic systems which made errors during the ball handover. Subjective and objective methods were used. The sample size of participants was N = 30 and the study was conducted in a between-subject design. Results show clear differences in perceived frustration in the two condition groups and different behavioral interactions were shown by the participants. Furthermore, frustration has a negative influence on interaction factors such as dominance and sense of control. The study provides important information concerning the influence of frustration on human–robot interaction for the requirements of a successful, natural, and social HRI. The results are discussed in favor of how a successful und effortless interaction between human and robot can be realized and what relevant factors, like appearance of the robot and influence of frustration on sense of control, have to be regarded. (shrink)

In the past decade, the fields of machine learning and artificial intelligence (AI) have seen unprecedented developments that raise human-machine interactions (HMI) to the next level.Smart machines, i.e., machines endowed with artificially intelligent systems, have lost their character as mere instruments. This, at least, seems to be the case if one considers how humans experience their interactions with them. Smart machines are construed to serve complex functions involving increasing degrees of freedom, and they generate solutions not fully anticipated by humans. (...) Consequently, their performances show a touch of action and even autonomy. HMI is therefore often described as a sort of “cooperation” rather than as a mere application of a tool. Some authors even go as far as subsuming cooperation with smart machines under the label ofpartnership, akin to cooperation between human agents sharing a common goal. In this paper, we explore how far the notion of shared agency and partnership can take us in our understanding of human interaction with smart machines. Discussing different topoi related to partnerships in general, we suggest that different kinds of “partnership” depending on the form of interaction between agents need to be kept apart. Building upon these discussions, we propose a tentative taxonomy of different kinds of HMI distinguishing coordination, collaboration, cooperation, and social partnership. (shrink)

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

Paul Churchland's book is an entertaining and instructive advertisement for a "neurocomputational" vision of how the brain works. While we agree with its general thrust, and commend its lucid pedagogy on a host of difficult topics, we note that such pedagogy often exploits artificially heightened contrast, and sometimes the result is a misleading caricature instead of a helpful simplification. In particular, Churchland is eager to contrast the explanation of consciousness that can be accomplished by his "aspiring new structural and dynamic (...) cognitive prototype: recurrent PDP networks" with what strikes him as the retrograde introduction by Dennett of a virtual von Neumannesque machine--a "failed prototype"--as the key element in an explanation of human consciousness. We will try to show that by oversimplifying Dennett's alternative, he has taken a potential supplement to his own view--a much needed supplement--and transformed it in his imagination into a subversive threat. In part 1, we will expose and correct the mistaken contrasts. In part 2, we will compare the performance of the two views on Churchland's list of seven features of consciousness any theory must account for, showing that Dennett's account provides more than Churchland has recognized, and indeed offers answers to key questions that Churchland's account is powerless to address. At that point, Churchland's project and Dennett's could be seen to collaborate in a useful division of labor instead of being in mortal combat, were it not for what appears to be a fairly major disagreement about consciousness in non-human animals. Part 3 briefly examines this issue. It may be due to a misunderstanding, which when cleared up might restore the happy prospect of unification. (shrink)

Cognitive science has been traditionally organized around the individual as the basic unit of cognition. Despite developments in areas such as communication, human–machine interaction, group behavior, and community organization, the individual-centric approach heavily dominates both cognitive research and its application. A promising direction for cognitive science is the study of augmented intelligence, or the way social and technological systems interact with and extend individual cognition. The cognitive science of augmented intelligence holds promise in helping society tackle major real-world challenges (...) that can only be discovered and solved by teams made of individuals and machines with complementary skills who can productively collaborate with each other. (shrink)

Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on search of an instance space (empirical exploration) and a hypothesis space (generation of theories). In scientific discovery, search must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This paper focuses especially on the processes for finding new (...) problem representations and new concepts, which are relatively new domains for research on discovery.Scientific discovery has usually been studied as an activity of individual investigators, but these individuals are positioned in a larger social structure of science, being linked by the blackboard of open publication (as well as by direct collaboration). Even while an investigator is working alone, the process is strongly influenced by knowledge and skills stored in memory as a result of previous social interaction. In this sense, all research on discovery, including the investigations on individual processes discussed in this paper, is social psychology, or even sociology. (shrink)

Philosophical discussion of Alan Turing’s writings on intelligence has mostly revolved around a single point made in a paper published in the journal Mind in 1950. This is unfortunate, for Turing’s reflections on machine (artificial) intelligence, human intelligence, and the relation between them were more extensive and sophisticated. They are seen to be extremely well-considered and sound in retrospect. Recently, IBM developed a question-answering computer (Watson) that could compete against humans on the game show Jeopardy! There are hopes it can (...) be adapted to other contexts besides that game show, in the role of a collaborator of, rather than a competitor to, humans. Another, different, research project—an artificial intelligence program put into operation in 2010—is the machine learning program NELL (Never Ending Language Learning), which continuously ‘learns’ by ‘reading’ massive amounts of material on millions of web pages. Both of these recent endeavors in artificial intelligence rely to some extent on the integration of human guidance and feedback at various points in the machine’s learning process. In this paper, I examine Turing’s remarks on the development of intelligence used in various kinds of search, in light of the experience gained to date on these projects. (shrink)

Paul Churchland's book (hereafter ER)is an entertaining and instructive advertisement for a "neurocomputational" vision of how the brain (and mind) works. While we agree with its general thrust, and commend its lucid pedagogy on a host of difficult topics, we note that such pedagogy often exploits artificially heightened contrast, and sometimes the result is a misleading caricature instead of a helpful simplification. In particular, Churchland is eager to contrast the explanation of consciousness that can be accomplished by his "aspiring new (...) structural and dynamic cognitive prototype: recurrent PDP networks" (p.266) with what strikes him as the retrograde introduction by Dennett of a virtual von Neumannesque machine--a "failed prototype"--as the key element in an explanation of human consciousness (in Consciousness Explained, 1991, hereafter, CE). We will try to show that by oversimplifying Dennett's alternative, he has taken a potential supplement to his own view--a much needed supplement--and transformed it in his imagination into a subversive threat. In part 1, we will expose and correct the mistaken contrasts. In part 2, we will compare the performance of the two views on Churchland's list of seven features of consciousness any theory must account for, showing that Dennett's account provides more than Churchland has recognized, and indeed offers answers to key questions that Churchland's account is powerless to address. At that point, Churchland's project and Dennett's could be seen to collaborate in a useful division of labor instead of being in mortal combat, were it not for what appears to be a fairly major disagreement about consciousness in non-human animals. Part 3 briefly examines this issue. It may be due to a misunderstanding, which when cleared up might restore the happy prospect of unification. (shrink)

In everyday interactions, humans naturally exhibit behavioral cues, such as gaze and head movements, that signal their intentions while interpreting the behavioral cues of others to predict their intentions. Such intention prediction enables each partner to adapt their behaviors to the intent of others, serving a critical role in joint action where parties work together to achieve a common goal. Among behavioral cues, eye gaze is particularly important in understanding a person's attention and intention. In this work, we seek to (...) quantify how gaze patterns may indicate a person's intention. Our investigation was contextualized in a dyadic sandwich-making scenario in which a “worker” prepared a sandwich by adding ingredients requested by a “customer.” In this context, we investigated the extent to which the customers' gaze cues serve as predictors of which ingredients they intend to request. Predictive features were derived to represent characteristics of the customers' gaze patterns. We developed a support vector machine-based (SVM-based) model that achieved 76% accuracy in predicting the customers' intended requests based solely on gaze features. Moreover, the predictor made correct predictions approximately 1.8 s before the spoken request from the customer. We further analyzed several episodes of interactions from our data to develop a deeper understanding of the scenarios where our predictor succeeded and failed in making correct predictions. These analyses revealed additional gaze patterns that may be leveraged to improve intention prediction. This work highlights gaze cues as a significant resource for understanding human intentions and informs the design of real-time recognizers of user intention for intelligent systems, such as assistive robots and ubiquitous devices, that may enable more complex capabilities and improved user experience. (shrink)

Enhancement technologies may someday grant us capacities far beyond what we now consider humanly possible. Nick Bostrom and Anders Sandberg suggest that we might survive the deaths of our physical bodies by living as computer emulations.­­ In 2008, they issued a report, or “roadmap,” from a conference where experts in all relevant fields collaborated to determine the path to “whole brain emulation.” Advancing this technology could also aid philosophical research. Their “roadmap” defends certain philosophical assumptions required for this technology’s success, (...) so by determining the reasons why it succeeds or fails, we can obtain empirical data for philosophical debates regarding our mind and selfhood. The scope ranges widely, so I merely survey some possibilities, namely, I argue that this technology could help us determine (1) if the mind is an emergent phenomenon, (2) if analog technology is necessary for brain emulation, and (3) if neural randomness is so wild that a complete emulation is impossible. (shrink)

This chapter discusses the question of whether we will ever have an Artificial General Superintelligence (AGSI) and how it will affect our species if it does so. First, it explores various proposed definitions of AGSI and the potential implications of its emergence, including the possibility of collaboration or conflict with humans, its impact on our daily lives, and its potential for increased creativity and wisdom. The concept of the Singularity, which refers to the hypothetical future emergence of superintelligent machines (...) that will take control of the world, is also introduced and discussed, along with criticisms of this concept. Second, it is considered the possibility of mind uploading (MU) and whether such MU would be a suitable means to achieve (true) immortality in this world—the ultimate goal of the proponents of this approach. It is argued that the technological possibility of achieving something like this is very remote, and that, even if it were ever achieved, serious problems would remain, such as the preservation of personal identity. Third, the chapter concludes arguing that the future we create will depend largely on how well we manage the development of AI. It is essential to develop governance of AI to ensure that critical decisions are not left in the hands of automated decision systems or those who create them. The importance of such governance lies not only in avoiding the dystopian scenarios of a future AGSI but also in ensuring that AI is developed in a way that benefits humanity. (shrink)

The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups. This renders the algorithmic decisions unfair relative to the standard fairness metrics in machine learning. In this paper, we defend the permissible use of affirmative algorithms; (...) that is, algorithms trained on diverse datasets that perform better for traditionally disadvantaged groups. Whilst such algorithmic decisions may be unfair, the fairness of algorithmic decisions is not the appropriate locus of moral evaluation. What matters is the fairness of final decisions, such as diagnoses, resulting from collaboration between clinicians and algorithms. We argue that affirmative algorithms can permissibly be deployed provided the resultant final decisions are fair. (shrink)

‘Computational thinking’ (CT) is highlighted in research literature, societal debates, and educational policies alike as being of prime significance in the 21st century. It is currently being introduced into K–12 (primary and secondary education) curricula around the world. However, there is no consensus on what exactly CT consists of, which skills it involves, and how it relates to programming. This article pinpoints four competing claims as to what constitutes the defining traits of CT. For each of the four claims, inherent (...) philosophical presuppositions are identified concerning the nature of thinking, problem-solving, and human–computer relationships. It is argued that most of these philosophical presuppositions are very similar to ones that Hubert Dreyfus and Donald Schön addressed forty to fifty years ago. Therefore, claims about the power of CT raise old discussions in a new disguise. Dreyfus and Schön argued that the presuppositions were wrong. Do their arguments hold and thus defeat contemporary claims about CT? Alternatively, has the technological development since the time of their writings proven them wrong? This article argues that it is necessary to heed the arguments of Dreyfus, Schön, and—later—Bent Flyvbjerg to ensure that CT curricula are built in accord with the value-rational nature of human practice, rather than on misconceived rationalizations of problem-solving, computer use, and computational aspects in the world. However, the pervasive integration of information technology in today's world raises new issues concerning human–machine collaborations that sidetrack the arguments of Dreyfus and Schön. A revised view of CT is required which focusses on articulating how humans can design for, partake in, and critically appraise human–machine collaborations. (shrink)

This paper investigates the transformative influence of generative AI on the arts, connecting it with Walter Benjamin's insights regarding the aura of art in the mechanical reproduction era. It scrutinizes how generative AI not only redefines art's traditional aura but also introduces a dynamic interplay between technological liberation and dependency. The analysis extends to the democratization of artistic expression and its broader societal impacts, highlighting a shift in art creation, perception, and interpretation in the digital age. This research encapsulates the (...) evolving nature of authenticity, creativity, and human–machine collaboration, marking a significant paradigm shift in art's conception and its role in society. (shrink)

This article examines types of abductive inference in Hegelian philosophy and machine learning from a formal comparative perspective and argues that Robert Brandom’s recent reconstruction of the logic of recollection in Hegel’s Phenomenology of Spirit may be fruitful for anticipating modes of collaborative abductive inference in human/A.I. interactions. Firstly, the argument consists of showing how Brandom’s reading of Hegelian recollection may be understood as a specific type of abductive inference, one in which the past interpretive failures and errors of a (...) community are explained hypothetically by way of the construction of a narrative that rehabilitates those very errors as means for the ongoing successful development of the community, as in Brandom’s privileged jurisprudential example of Anglo-American case law. Next, this Hegelian abductive dynamic is contrasted with the error-reducing backpropagation algorithms characterizing many current versions of machine learning, which can be understood to perform abductions in a certain sense for various problems but not in the full self-constituting communitarian mode of creative recollection canvassed by Brandom. Finally, it is shown how the two modes of “error correction” may possibly coordinate successfully on certain types of abductive inference problems that are neither fully recollective in the Hegelian sense nor algorithmically optimizable. (shrink)

Scholars have explored the influence of the field of cybernetics on scientific thought and disciplines. However, from the inception of the field, ‘‘cyberneticians’’ had explicitly envisioned applications reaching beyond the purview of scientific disciplines; cybernetics was remarkable for its portability and potential application in a wide variety of contexts. This article explores connections between cybernetics and experimental music from 1950-1980, which was a period of experimentation with electronic techniques in recording, composition, and sound production and manipulation. Examples include musicians, engineers, (...) instrument builders, composers, and scientists in collaboration with musicians who invoked cybernetic themes in their work. These uses of cybernetics were more diverse than accounts of cybernetics within the sciences suggest, presenting a major difficulty in addressing cybernetics as a homogeneous or monolithic discourse. In particular, cybernetic discourse in music often exhibited themes of openness and indeterminacy, rather than the ‘‘command and control’’ of the ‘‘closed world.’’. (shrink)

In the 1990’s, Robotics began to design a new robot aimed at industries (primarily automotive) that worked and interacted with humans outside the cage, thereby replacing traditional _robots_ for some specific duties. This _robot_ is therefore called _co-bot_ (_collaborative_ and _robot)._ Also in the 1990’s, Robotics designed the _social robot_ (for which we propose the neologism _so-bot),_ aimed at assisting humans and keeping them company. The sociality of the _sobots_ lies in their ability to follow the rules of human social (...) life, make decisions independently, and respect the roles assigned to them. Scientific literature usually keeps the terms _collaborative_ and _social robot_ distinct as if they indicated different and separate concepts. We question this separation and affirm that to collaborate (from the Latin _cum-laboro_) means to interact with someone while respecting their nature. Collaboration is that particular form of sociality that relates to work activity. From this it follows that the _cobot_ is essentially social and that _cobots_ and _sobots_ belong to the same category that we call _co-s-bots_ (_collaborative social robots_). In other words, _cobots_ and _sobots_ are two types of _cosbots,_ as the flea and the elephant are two types of animals. The difference between _cobot_ and _sobot_ is given by the development of AI. Both are _potentially_ social, that is, _potentially_ capable of interacting and making decisions independently; but while the _cobot_ is social in potency, the _sobot_ is social ‘in act’. With Aristotelian terminology we can therefore say that the _cobot_ is a _sobot_ in power, while the _sobot_ is a _cobot_ in act. We call this new concept ‘_cobot ontology_’. Such an ontology makes it possible to classify _cobots_ according to the degree of development of AI, just as living beings are classified according to the level of intelligence developed.To teach the _cosbot_ to interact with humans, engineers use some results of neuroscientific research such as mirror neurons and the _embodied Mind_. The use of these models should encourage machine self-learning. Self-learning means autonomy, and autonomy needs strong AI development. It is becoming increasingly clear that autonomy is the condition of the sociality of the _sobot_. The article thus concludes that the relationship between _cobot_ and _sobot_ is the identification of a more general _robot_-_automaton_ (_rabota_-_automatos_) relationship which, in the writer’s opinion, is the essential basis and driving force behind the entire history of Robotics. (shrink)

Different people have different perceptions about artificial intelligence (AI). It is extremely important to bring together all the alternative frames of thinking—from the various communities of developers, researchers, business leaders, policymakers, and citizens—to properly start acknowledging AI. This article highlights the ‘fruitful collaboration’ that sociology and AI could develop in both social and technical terms. We discuss how biases and unfairness are among the major challenges to be addressed in such a sociotechnical perspective. First, as intelligent machines reveal their (...) nature of ‘magnifying glasses’ in the automation of existing inequalities, we show how the AI technical community is calling for transparency and explainability, accountability and contestability. Not to be considered as panaceas, they all contribute to ensuring human control in novel practices that include requirement, design and development methodologies for a fairer AI. Second, we elaborate on the mounting attention for technological narratives as technology is recognized as a social practice within a specific institutional context. Not only do narratives reflect organizing visions for society, but they also are a tangible sign of the traditional lines of social, economic, and political inequalities. We conclude with a call for a diverse approach within the AI community and a richer knowledge about narratives as they help in better addressing future technical developments, public debate, and policy. AI practice is interdisciplinary by nature and it will benefit from a socio-technical perspective. (shrink)

The term ‘intelligence’ as used in this paper refers to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding of world actors (...) and their behaviors can be developed and updated. Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts. Of particular importance to the Cognitive Process Ontology is the class Representation that is Warranted. Such a representation is descriptive in nature and deserving of trust in its veridicality. The latter is because a Representation that is Warranted is always produced by a process that was vetted (or successfully designed) to reliably produce veridical representations. As such, Representations that are Warranted are what in other contexts we might refer to as ‘items of knowledge’. (shrink)

This paper deals with the birth of Roboethics. Roboethics is the ethics inspiring the design, development and employment of Intelligent Machines. Roboethics shares many 'sensitive areas' with Computer Ethics, Information Ethics and Bioethics. It investigates the social and ethical problems due to the effects of the Second and Third Industrial Revolutions in the Humans/Machines interaction's domain. Urged by the responsibilities involved in their professions, an increasing number of roboticists from all over the world have started - in cross-cultural collaboration (...) with scholars of Humanities - to thoroughly develop the Roboethics, the applied ethics that should inspire the design, manufacturing and use of robots. The result is the Roboethics Roadmap. (shrink)

The design of collaborative robotics, such as driver-assisted operations, engineer a potential automation of decision-making predicated on unobtrusive data gathering of human users. This form of ‘somatic surveillance’ increasingly relies on behavioural biometrics and sensory algorithms to verify the physiology of bodies in cabin interiors. Such processes secure cyber-physical space, but also register user capabilities for control that yield data as insured risk. In this technical re-formation of human–machine interactions for control and communication ‘a dissonance of attribution’ :7684, 2019. (...) https://doi.org/10.1073/pnas.1805770115) is created between perceptions of phenomena, materials and decision-making. This reconfigures relations not only between humans and machines, objects and subjects, but possibly disrupts attributive functions in the social system of Law. What it requires is shifting a legal accountability for action from a sovereignty of the human to a new materialist account based on a ‘cognitive assemblage’ between physiological data, computation and algorithmic sensing. This paper investigates the function of law as a guidance system to acknowledge this account of sensory and algorithmic computation as autonomous ‘sensing agents’ that may be accountable in situations of risk. This assemblage of robotic computation and sensory determination requires a clearer legal differentiation across the current static terminologies of person, property, liability and rights that maintain strict separations of object from subject. To neglect this, we argue, law will solely impute attributions of error to humans despite evidence of operation via mutual control. (shrink)

Humanity's creative role within the living pattern of nature. Explores important scientific discoveries that reveal the self-organizing intelligence at the heart of nature. Examines the idea of a living cosmos from its roots in the earliest cultures, to its eclipse during the Scientific Revolution, to its return today. Reveals ways to reengage our creative partnership with nature and collaborate with nature's intelligence. For millennia the world was seen as a creative, interconnected web of life, constantly growing, developing, and restoring itself. (...) But with the arrival of the Scientific Revolution in the 16th and 17th centuries, the world was viewed as a lifeless, clock-like mechanism, bound by the laws of classical physics. Intelligence was a trait ascribed solely to human beings, and thus humanity was viewed as superior to and separate from nature. Today new scientific discoveries are reviving the ancient philosophy of a living, interconnected cosmos, and humanity is learning from and collaborating with nature's intelligence in new, life-enhancing ways, from ecological design to biomimicry. Drawing upon the most important scientific discoveries of recent times, David Fideler explores the self-organizing intelligence at the heart of nature and humanity's place in the cosmic pattern. He examines the ancient vision of the living cosmos from its roots in the "world soul" of the Greeks and the alchemical tradition, to its eclipse during the Scientific Revolution, to its return today. He explains how the mechanistic worldview led to humanity's profound sense of alienation, for if the universe only functioned as a machine, there was no longer any room for genuine creativity or spontaneity. He shows how this isn't the case and how, even at the molecular level, natural systems engage in self-organization, self-preservation, and creative problem solving, mirroring the ancient idea of a creative intelligence that exists deep within the heart of nature. Revealing new connections between science, religion, and culture, Fideler explores how to reengage our creative partnership with nature and new ways to collaborate with nature's intelligence. (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)

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)

This paper presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, finishing, deburring, etc. The proposed scheme is based on task priority and non-conventional sliding mode control. Furthermore, the proposal includes two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The applicability and feasibility of the proposed collaborative (...) solution for robotic surface treatment are substantiated by experimental results using a redundant 7R manipulator: the Sawyer collaborative robot. (shrink)

This paper presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, finishing, deburring, etc. The proposed scheme is based on task priority and non-conventional sliding mode control. Furthermore, the proposal includes two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The applicability and feasibility of the proposed collaborative (...) solution for robotic surface treatment are substantiated by experimental results using a redundant 7R manipulator: the Sawyer collaborative robot. (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)

We ask how to use machine learning to expand observability, which presently depends on human learning that informs conceivability. The issue is engaged by considering the question of correspondence between conceived observability counterfactuals and observable, yet so far unobserved or unconceived, states of affairs. A possible answer lies in importing out of reference frame content which could provide means for conceiving further observability counterfactuals. They allow us to define high-fidelity observability, increasing the level of correspondence in question. To achieve high-fidelity (...) observability, we propose to use generative machine learning models as the providers of the out of reference frame content. From an applied point of view, such a role of generative machine learning models shows an emerging dimension of human-machine cooperation. (shrink)

The worlds of Dune forbid the creation of “thinking machines,” due to an ancient war, called the Butlerian Jihad, which was fought to keep humans from using such machines. The relationship between humanity and forbidden technology in Dune touches on two basic possibilities for the relationship of humans and technology: social construction of technology and technological determinism. Life on Arrakis and under the Imperium is filled with technologies that range from the very realistic to the fantastical. Societies in the Dune (...) universe tend to value a variety of everyday and cutting‐edge technologies. Understanding the reasons why some technology is forbidden in Dune depends on understanding how Herbert views human beings. The cultures of the Dune universe place great cultural value on technology in its importance for everyday lives. (shrink)

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)

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)

In this article a look is taken at some of the different ways in which the human–machine divide is rapidly disappearing. In each case the technical basis is described and then some of the implications are also considered. In particular results from experiments are discussed in terms of their meaning and application possibilities. The article is written from the perspective of scientific experimentation opening up realistic possibilities to be faced in the future, rather than giving conclusive comments. In each (...) case consideration is also given to some of the philosophical questions that arise. (shrink)