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)

The popularization of intelligent machines such as service robot and industrial robot will make human–machine interaction, an essential work mode. This requires employees to adapt to the new work content through learning. However, the research involved human–machine interaction that how influences the employee’s learning is still rarely. This paper was to reveal the relationship between human–machine interaction and employee’s learning from the perspective of job characteristics and competence perception of employees. We sent questionnaire to (...) 500 employees from 100 artificial intelligence companies in China and received 319 valid and complete responses. Then, we adopted a hierarchical regression for the test. Empirical results show that human–machine interaction has a U-shaped curvilinear relationship with employee learning, and employee’s vitality mediates the curvilinear relationship. In addition, job characteristics moderate the U-shaped curvilinear relationship between human–machine interaction and employee’s vitality, especially the results of moderating effects varying with employee’s competence perception. Exploring the mechanism of the effect of human–machine interaction on employee’s learning enriches the socially embedded model. Moreover, it provides managerial implications how to enhance individual adaptability with the introduction of AI into firms. However, our research focuses more on the impact of human–machine interaction on employees at the initial stage of AI development, and the level of machine intelligence in various industries will reach a high degree of autonomy in the future. The future research can explore the impact of human–machine interaction on individual’s behavior at different stages, and the results may vary depending on the technologies mastered by different individuals. The study has theoretical and practical significance to human–machine interaction literature by underscoring the important of individual’s behavior among individuals with different skills. (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)

We used a new method called “Ghost-in-the-Machine” (GiM) to investigate social interactions with a robotic bartender taking orders for drinks and serving them. Using the GiM paradigm allowed us to identify how human participants recognize the intentions of customers on the basis of the output of the robotic recognizers. Specifically, we measured which recognizer modalities (e.g., speech, the distance to the bar) were relevant at different stages of the interaction. This provided insights into human social behavior necessary (...) for the development of socially competent robots. When initiating the drink-order interaction, the most important recognizers were those based on computer vision. When drink orders were being placed, however, the most important information source was the speech recognition. Interestingly, the participants used only a subset of the available information, focussing only on a few relevant recognizers while ignoring others. This reduced the risk of acting on erroneous sensor data and enabled them to complete service interactions more swiftly than a robot using all available sensor data. We also investigated socially appropriate response strategies. In their responses,the participants preferred to use the same modality as the customer’s requests, e.g., they tended to respond verbally to verbal requests. Also, they added redundancy to their responses, for instance by using echo questions. We argue that incorporating the social strategies discovered with the GiM paradigm in multimodal grammars of human–robot interactions improves the robustness and the ease-of-use of these interactions, and therefore provides a smoother user experience. (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)

Can machines simulate, express or even have emotions? Is it a good to build such machines? How do humans react emotionally to them and how should such devices be treated from a moral point of view? This volume addresses these and related questions by bringing together perspectives from affective computing and emotional human-machine interaction, combining technological approaches with those from the humanities and social sciences. It thus relates disciplines such as philosophy, computer science, technology, psychology, sociology, design, and (...) art. The volume offers readers interested in the phenomenon of emotional machines new perspectives from a variety of disciplines and addresses fundamental questions that will become pressing in the foreseeable future as emotional machines increasingly populate our everyday lives. (shrink)

The focus of the following article is on the use of new robotic systems in the manufacturing industry with respect to the social dimension. Since “intuitive” human–machine interaction (HMI) in robotic systems becomes a significant objective of technical progress, new models of work organization are needed. This hypothesis will be investigated through the following two aims: The first aim is to identify relevant research questions related to the potential use of robotic systems in different systems of work (...) organization at the manufacturing shop-floor level. The second aim is to discuss the conceptualization of (old) organizational problems of human–robot interaction (HRI). In this context, the article reflects on the limits of cognitive and perceptual workload for robot operators in complex working systems. This will be particularly relevant whenever more robots with different “roles” are to be increasingly used in the manufacturing industry. The integration of such complex socio-technical systems needs further empirical and conceptual research with regard to “social” aspects of the technical dimension. Future research should, therefore, also integrate economic and societal issues to understand the full dimensions of new human–robot interaction in industry today. (shrink)

New forms of artificial intelligence on the one hand and the ubiquitous networking of “everything with everything” on the other hand characterize the fourth industrial revolution. This results in a changed understanding of human–machine interaction, in new models for production, in which man and machine together with virtual agents form hybrid teams. The empirical study “Socializing with robots” aims to gain insight especially into conditions of development and processes of hybrid human–machine teams. In the experiment, human–robot actions (...) and interactions were closely observed in a virtual environment. Robots as partners differed in shape and behavior. Participants were instructed to achieve an objective that could only be achieved via close teamwork. This paper unites different aspects from core disciplines of social robotics and psychology contributing to anthropomorphization with the empirical insights of the experiment. It focuses on the psychological effects on anthropomorphization and mechanization, taking the inter- and transdisciplinary field of social robotics as a starting point. (shrink)

Gaze following occurs automatically in social interactions, but the degree to which gaze is followed depends on whether an agent is perceived to have a mind, making its behavior socially more relevant for the interaction. Mind perception also modulates the attitudes we have towards others, and deter-mines the degree of empathy, prosociality and morality invested in social interactions. Seeing mind in others is not exclusive to human agents, but mind can also be ascribed to nonhuman agents like (...) robots, as long as their appearance and/or behavior allows them to be perceived as intentional beings. Previous studies have shown that human appearance and reliable behavior induce mind perception to robot agents, and positively affect attitudes and performance in human-robot interaction. What has not been investigated so far is whether different triggers of mind perception have an independent or interactive effect on attitudes and performance in human-robot interaction. We examine this question by manipulating agent appearance (human vs. robot) and behavior (reliable vs. random) within the same paradigm and examine how congruent (human/reliable vs. robot/random) versus incongruent (human/random vs. robot/reliable) combinations of these triggers affect performance (i.e., gaze fol-lowing) and attitudes (i.e., agent ratings) in human-robot interaction. The results show that both ap-pearance and behavior affect human-robot interaction but that the two triggers seem to operate in iso-lation, with appearance more strongly impacting attitudes, and behavior more strongly affecting per-formance. The implications of these findings for human-robot interaction are discussed. (shrink)

Anthropomorphism is a phenomenon that describes the human tendency to see human-like shapes in the environment. It has considerable consequences for people’s choices and beliefs. With the increased presence of robots, it is important to investigate the optimal design for this tech- nology. In this paper we discuss the potential benefits and challenges of building anthropomorphic robots, from both a philosophical perspective and from the viewpoint of empir- ical research in the fields of human–robot interaction and social psychology. (...) We believe that this broad investigation of anthropomorphism will not only help us to understand the phenomenon better, but can also indicate solutions for facil- itating the integration of human-like machines in the real world. (shrink)

We use speech shadowing to create situations wherein people converse in person with a human whose words are determined by a conversational agent computer program. Speech shadowing involves a person (the shadower) repeating vocal stimuli originating from a separate communication source in real-time. Humans shadowing for conversational agent sources (e.g., chat bots) become hybrid agents ("echoborgs") capable of face-to-face interlocution. We report three studies that investigated people’s experiences interacting with echoborgs and the extent to which echoborgs pass as autonomous humans. (...) First, participants in a Turing Test spoke with a chat bot via either a text interface or an echoborg. Human shadowing did not improve the chat bot’s chance of passing but did increase interrogators’ ratings of how human-like the chat bot seemed. In our second study, participants had to decide whether their interlocutor produced words generated by a chat bot or simply pretended to be one. Compared to those who engaged a text interface, participants who engaged an echoborg were more likely to perceive their interlocutor as pretending to be a chat bot. In our third study, participants were naïve to the fact that their interlocutor produced words generated by a chat bot. Unlike those who engaged a text interface, the vast majority of participants who engaged an echoborg neither sensed nor suspected a robotic interaction. These findings have implications for android science, the Turing Test paradigm, and human-computer interaction. The human body, as the delivery mechanism of communication, fundamentally alters the social psychological dynamics of interactions with machine intelligence. (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 (an anthropomorphic robot and a robotic arm). 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- (FRUST) and a no frustration- (NOFRUST) 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 wasN= 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 (HRI) for the requirements of a successful, natural, and social HRI. The results (qualitative and quantitative) 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 recent developments in Artificial Intelligence and especially in robotics we can observe a tendency towards building intelligent artefacts that are meant to be social, to have ‘human social’ characteristics like emotions, the ability to conduct dialogue, to learn, to develop personality, character traits, and social competencies. Care, entertainment, pet and educational robots are conceptualised as friendly, understanding partners and credible assistants which communicate ‘naturally’ with users, show emotions and support them in everyday life. Social robots (...) are often designed to interact physically, affectively and socially with humans in order to learn from them. To achieve this goal, roboticists often model the human‐robot interaction on early caregiver‐infant interactions. In this paper I want to analyse prominent visions of these ‘socio‐emotional’ machines as well as early prototypes and commercial products with regard to the human‐machine interface. By means of this I will ask how feminist critiques of technology could be applied to the field of social robotics in which concepts like sociality or emotion are crucial elements while, at the same time, these concepts play an important role in feminist critiques of technology. (shrink)

The construction of embodied conversational agents - robots as well as avatars - seem to be a new challenge in the field of both cognitive AI and human-computer-interface development. On the one hand, one aims at gaining new insights in the development of cognition and communication by constructing intelligent, physical instantiated artefacts. On the other hand people are driven by the idea, that humanlike mechanical dialog-partners will have a positive effect on human-machine-communication. In this contribution I put for discussion whether (...) the visions of scientist in this field are plausible and which problems might arise by the realization of such projects. (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)

This paper examines Harold Garfinkel’s work with ELIZA and a related program LYRIC from 1967 to 1969. AI researchers have tended to treat successful human–machine interaction as if it relied primarily on non-human machine characteristics, and thus the often-reported attribution of human-like qualities to communication with computers has been criticized as a misperception—and humans who make such reports referred to as “deluded.” By contrast Garfinkel, building on two decades of prior research on information and communication, argued that the (...) ELIZA and the LYRIC “chatbots” were achieving interactions that felt human to many users by exploiting human sense-making practices. In keeping with his long-term practice of using “trouble” as a way of discovering the taken-for-granted practices of human sense-making, Garfinkel designed scripts for ELIZA and LYRIC that he could disrupt in order to reveal how their success depended on human social practices. Hence, the announcement “Machine Down” by the chatbot was a desired result of Garfinkel’s interactions with it. This early (but largely unknown) research has implications not only for understanding contemporary AI chatbots, but also opens possibilities for respecifying current information systems design and computational practices to provide for the design of more flexible information objects. (shrink)

This paper analyzes epistemological and ontological dimensions of Human-Computer Interaction (HCI) through an analysis of the functions of computer systems in relation to their users. It is argued that the primary relation between humans and computer systems has historically been epistemic: computers are used as information-processing and problem-solving tools that extend human cognition, thereby creating hybrid cognitive systems consisting of a human processor and an artificial processor that process information in tandem. In this role, computer systems extend human cognition. (...) Next, it is argued that in recent years, the epistemic relation between humans and computers has been supplemented by an ontic relation. Current computer systems are able to simulate virtual and social environments that extend the interactive possibilities found in the physical environment. This type of relationship is primarily ontic, and extends to objects and places that have a virtual ontology. Increasingly, computers are not just information devices, but portals to worlds that we inhabit. The aforementioned epistemic and ontic relationships are unique to information technology and distinguish human-computer relationships from other human-technology relationships. (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)

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)

Interactions between humans and bots are increasingly common online, prompting some legislators to pass laws that require bots to disclose their identity. The Turing test is a classic thought experiment testing humans’ ability to distinguish a bot impostor from a real human from exchanging text messages. In the current study, we propose a minimal Turing test that avoids natural language, thus allowing us to study the foundations of human communication. In particular, we investigate the relative roles of conventions and reciprocal (...) interaction in determining successful communication. Participants in our task could communicate only by moving an abstract shape in a 2D space. We asked participants to categorize their online social interaction as being with a human partner or a bot impostor. The main hypotheses were that access to the interaction history of a pair would make a bot impostor more deceptive and interrupt the formation of novel conventions between the human participants. Copying their previous interactions prevents humans from successfully communicating through repeating what already worked before. By comparing bots that imitate behavior from the same or a different dyad, we find that impostors are harder to detect when they copy the participants’ own partners, leading to less conventional interactions. We also show that reciprocity is beneficial for communicative success when the bot impostor prevents conventionality. We conclude that machine impostors can avoid detection and interrupt the formation of stable conventions by imitating past interactions, and that both reciprocity and conventionality are adaptive strategies under the right circumstances. Our results provide new insights into the emergence of communication and suggest that online bots mining personal information, for example, on social media, might become indistinguishable from humans more easily. (shrink)

A recurrent theme of human–machine interaction is how interaction is defined and what kind of information is relevant for successful communication. In accordance with the theoretical strategies of social cognition and technical philosophy, we propose a pragmatic interactive approach, to understand the concept of information in human–machine interaction. We start with the investigation of interpersonal interaction and human–machine interaction by concerning triangulation as guiding principle. To illustrate human–machine interaction, we (...) will mainly focus on the interactive relationship between human cognitive skills and their technological environments. In triadic interaction, the concept of information cannot only be understood as a syntactic transformation as in the dyadic relationship. We argue that information as pragmatic action patterns can be understood as physically embodied units that is not purely formal but generated in interactive processes and stabilized in use. By exploring how the concept of affordance is applied in psychology, anthropology and human-centered design, we provide a notion of pragmatic information, focusing on users in the development and use of machines or software systems. The triadic interaction of end user, development team, and product leads to a successful human–machine interaction. This theoretical framework enables us to understand how the new socio-technological environments expand our scope of action and how we form, interpret, and learn to deal with patterns of action among ourselves and with machines. (shrink)

This paper analyzes responsible human–machine interaction concerning artificial neural networks (ANNs) and large language models (LLMs) by considering the extension of human agency and autonomy by means of artificial intelligence (AI). Thereby, the paper draws on the sociological concept of “interobjectivity,” first introduced by Bruno Latour, and applies it to technologically situated and interconnected agency. Drawing on Don Ihde’s phenomenology of human-technology relations, this interobjective account of AI allows to understand human–machine interaction as embedded in the (...) social lifeworld. Finally, the paper develops a connectionist account of responsible AI, thereby focusing on patterns such as social goals and actions. (shrink)

In this paper, we theoretically address the relevance of unintentional and inconsistent interactional elements in human–robot interactions. We argue that elements failing, or poorly succeeding, to reproduce a humanlike interaction create significant consequences in human–robot relational patterns and may affect human–human relations. When considering social interactions as systems, the absence of a precise interactional element produces a general reshaping of the interactional pattern, eventually generating new types of interactional settings. As an instance of this dynamic, we study the (...) absence of metacommunicative abilities in social artifacts. Then, we analyze the pragmatic consequences of the aforementioned absence through the lens of Paul Watzlawick’s interactionist theory. We suggest that a fixed complementary interactional setting may be produced because of the asymmetric understanding, between robots and humans, of metacommunication. We highlight the psychological implications of this interactional asymmetry within Jessica Benjamin’s concept of “mutual recognition”. Finally, we point out the possible shift of dysfunctional interactional patterns from human–robot interactions to human–human ones. (shrink)

For a robot to cohabit with people, it should be able to learn people’s nonverbal social behavior from experience. In this paper, we propose a novel machine learning method for recognizing gestures used in interaction and communication. Our method enables robots to learn gestures incrementally during human–robot interaction in an unsupervised manner. It allows the user to leave the number and types of gestures undefined prior to the learning. The proposed method (HB-SOINN) is based on a self-organizing (...) incremental neural network and the hidden Markov model. We have added an interactive learning mechanism to HB-SOINN to prevent a single cluster from running into a failure as a result of polysemy of being assigned more than one meaning. For example, a sentence: “Keep on going left slowly” has three meanings such as, “Keep on (1)”, “going left (2)”, “slowly (3)”. We experimentally tested the clustering performance of the proposed method against data obtained from measuring gestures using a motion capture device. The results show that the classification performance of HB-SOINN exceeds that of conventional clustering approaches. In addition, we have found that the interactive learning function improves the learning performance of HB-SOINN. (shrink)

Social appropriateness is an important topic – both in the human-human interaction (HHI), and in the human-machine interaction (HMI) context. As sociosensitive and socioactive assistance systems advance, the question arises whether a machine’s behavior should include considerations regarding social appropriateness. However, the concept of social appropriateness is difficult to define, as it is determined by multiple aspects. Thus, to date, a unified perspective, encompassing and combining multidisciplinary findings, is missing. When translating results from HHI to (...) HMI, it remains unclear whether such insights into the dynamics of social appropriateness between humans may in fact apply to sociosensitive and socioactive assistance systems. To shed light on this matter, we propose the Five Factor Model of Social Appropriateness (FASA) which provides a multidisciplinary perspective on the notion of social appropriateness and its implementation into technical systems. Finally, we offer reflections on the applicability and ethics of the FASA Model, highlighting both strengths and limitations of the framework. (shrink)

Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds (...) of individuals correlate in practice with different modes of human–computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia. (shrink)

Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human-autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its (...) relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams’ coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human-autonomy teams that augment team interactions using coordination-based measures. (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)

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)

As robots increasingly become part of our everyday lives, questions arise with regards to how to approach them and how to understand them in social contexts. The Western history of human–robot relations revolves around competition and control, which restricts our ability to relate to machines in other ways. In this study, we take a relational approach to explore different manners of socializing with robots, especially those that exceed an instrumental approach. The nonhuman subjects of this study are built to (...) explore non-purposeful behavior, in an attempt to break away from the assumptions of utility that underlie the hegemonic human–machine interactions. This breakaway is accompanied by ‘learning to be attuned’ on the side of the human subjects, which is facilitated by continuous relations at the level of everyday life. Our paper highlights this ground for the emergence of meanings and questions that could not be subsumed by frameworks of control and domination. The research-creation project Machine Ménagerie serves as a case study for these ideas, demonstrating a relational approach in which the designer and the machines co-constitute each other through sustained interactions, becoming attuned to one another through the performance of research. Machine Ménagerie attempts to produce affective and playful—if not unruly—nonhuman entities that invite interaction yet have no intention of serving human social or physical needs. We diverge from other social robotics research by creating machines that do not attempt to mimic human social behaviours. (shrink)

Putting laypeople in an active role as direct expert contributors in the design of service robots becomes more and more prominent in the research fields of human–robot interaction and social robotics. Currently, though, HRI is caught in a dilemma of how to create meaningful service robots for human social environments, combining expectations shaped by popular media with technology readiness. We recapitulate traditional stakeholder involvement, including two cases in which new intelligent robots were conceptualized and realized for close (...) interaction with humans. Thereby, we show how the robot narrative together with aspects of power balancing stakeholders, such as hardware constraints and missing perspectives beyond primary users, and the adaptivity of robots through machine learning that creates unpredictability, pose specific challenges for participatory design processes in HRI. We conclude with thoughts on a way forward for the HRI community in developing a culture of participation that considers humans when conceptualizing, building, and using robots. (shrink)

This article synthesizes the current theoretical attempts to understand human-machine interactions and introduces seven premises to understand our emerging dynamics with increasingly competent, pervasive, and instantly accessible algorithms. The hope that these seven premises can build toward a social theory of human-AI cocreation. The focus on human-AI cocreation is intended to emphasize two factors. First, is the fact that our machine learning systems are socialized. Second, is the coevolving nature of human mind and AI systems as smart devices form (...) an indispensable part of our cognitive scaffolds, thus, shaping how we perceive the world and ourselves. The seven premises include: primacy of social structures; human’s desire for freedom and autonomy; AI systems will form inseparable parts of our cognitive/affective scaffolds and can change our self-understanding; philosophy and humanistic foundations on human flourishing as a guide to human-AI interaction; mindsponge information-filtering process; acculturative process of how values change and emerge from human-AI interaction; overlapping Venn diagram of the human-human, human-nature, and human-machine interactions. This article concludes with a discussion on human agency in our entanglements with socialized machines and the illusoriness of the Cartesian agent view of the mind. (shrink)

Interactions between an intelligent software agent and a human user are ubiquitous in everyday situations such as access to information, entertainment, and purchases. In such interactions, the ISA mediates the user’s access to the content, or controls some other aspect of the user experience, and is not designed to be neutral about outcomes of user choices. Like human users, ISAs are driven by goals, make autonomous decisions, and can learn from experience. Using ideas from bounded rationality, we frame these interactions (...) as instances of an ISA whose reward depends on actions performed by the user. Such agents benefit by steering the user’s behaviour towards outcomes that maximise the ISA’s utility, which may or may not be aligned with that of the user. Video games, news recommendation aggregation engines, and fitness trackers can all be instances of this general case. Our analysis facilitates distinguishing various subcases of interaction, as well as second-order effects that might include the possibility for adaptive interfaces to induce behavioural addiction, and/or change in user belief. We present these types of interaction within a conceptual framework, and review current examples of persuasive technologies and the issues that arise from their use. We argue that the nature of the feedback commonly used by learning agents to update their models and subsequent decisions could steer the behaviour of human users away from what benefits them, and in a direction that can undermine autonomy and cause further disparity between actions and goals as exemplified by addictive and compulsive behaviour. We discuss some of the ethical, social and legal implications of this technology and argue that it can sometimes exploit and reinforce weaknesses in human beings. (shrink)

Spoken communication between social robotic devices, powered by generative AI tools such as ChatGPT, and the senior population offers great potential for researching social interaction and robot identity perceptions as well as exploring the potential opportunities and challenges when implementing this human-machine interactions in real life situations and health care. In this paper we explore people’s perceptions of the social robot Furhat when administering verbal tasks similar to those used in screening for Alzheimer’s disease. We describe (...) the Slovak system mounted on the robot that includes a speech recognizer, the scenarios powered by generative large language model ChatGPT, and a speech synthesizer. We tested the functionality of the proposed approach with two groups of participants: attendees of a large science fair and a scientific conference. The observations from 87 collected questionnaires suggest good potential and applicability of such an approach for the given task and more positive attitudes of older subjects compared to younger ones. (shrink)

The contemporary digital reality is inconceivable without artificial intelligence (AI), which has become disseminated across all cultural practices, from scientific and artistic endeavors to everyday activities. AI increasingly functions as an agent of communication and decision-making, gradually surpassing human capabilities across nearly all competencies. The information flows of this new reality can only be navigated through hybrid systems based on post-critical rationality, which inherently introduces an irreducible element of uncertainty and risk in human-machine environments. The article proposes examining the techno-subject (...) through the lens of activity theory and the multiple types of rationality it generates. This framework facilitates the analysis of sociocultural and anthropological implications arising from AI’s integration with human domains, while addressing the existential challenges inherent in constructing a harmonious hybrid society. Beyond V.S. Stepin’s types of scientific rationality, the author builds upon previously introduced forms of rationality: post-critical, object-oriented, instrumental, subjective, results-oriented, creative, and autopoietic. This theoretical framework facilitates a substantive discussion of various manifestations of AI subjectivity, including its generalized embodiment and creative specificity. The demarcation of dominance domains between natural intelligence and AI in the intellectual sphere is proposed to be resolved on the basis of their heuristic potentials. The author maintains that natural intelligence invariably possesses superior capacity in this regard. The article examines approaches to risk assessment in AI implementation strategies, focusing on criteria for preserving anthropological and sociocultural profiles in the development of hybrid society. Advancing the concept of friendly AI is substantiated as essential, with consideration given not only to technological but also to anthropological aspects of human–machine interaction. The author advocates for the development of social examination institutions as regulatory mechanisms for natural–artificial intelligence interaction and anthropological–technological subject interfaces. (shrink)

In the paper, the touchstone points of the project “Towards an agency-based philosophy of (advanced) technology” are outlined. The main plot of this elaboration concerns human-machine interactions and appropriate interpretation of reciprocal aligning, adapting within involved into such interactions agents; as well as the status as such of being an agent. Into the theoretical and historical background of the project such spheres as Philosophy of Science, Philosophy of Technology, Philosophy of Engineering and Design Technological Actions, STS (Science and Technology Studies), (...) Applied Ethics etc. could be invited. To treat agency as technology, reestablish the role of agency in technology is the most ambitious goal of the project: ‘activity as technology’ focuses on activities through technologies. The terms «agency» and «activity» are used in this paper synonymously with the basic Aristotelian meaning of agent’s potential capacity to act. The proposed by the author theory of action and agency (and correspondently defended in 2016 dissertation of Doctor of Philosophical Science) is to be applied into philosophical reflections about various problems of dealing with currently continually appearing flourishing fields of artificial intelligence, machine learning (including deep learning methods and simulation methods), inventing computers and codes, numerically controlled machines and robots, computer-chip equipped devices, smart objects etc. (shrink)

The paper adopts an inter-theoretical socio-cultural and -material perspective on the relationship between human + machine learning to propose a new way to investigate the human + machine assistive assemblages emerging in professional work (e.g. medicine, architecture, design and engineering). Its starting point is Hutchins’s (1995a) concept of ‘distributed cognition’ and his argument that his concept of ‘cultural ecosystems’ constitutes a unit of analysis to investigate collective human + machine working and learning (Hutchins, Philos Psychol 27:39–49, 2013). It argues that: (...) (i) the former offers a way to reveal the cultural constitution of and enactment of human + machine cognition and, in the process, the limitations of the computational and connectionist assumptions about learning that underpin, respectively, good old-fashioned AI and deep learning; and (2) the latter offers a way to identify, when amplified with insights from Socio-Materialism and Cultural-Historical Activity Theory, how ML is further rearranging and reorganising the distributed basis of cognition in assistive assemblages. The paper concludes by outlining a set of conjectures researchers that could use to guide their investigations into the ongoing design and deployment of HL + ML assemblages and challenges associated with the interaction between HL + ML. (shrink)

Recent philosophy of mind has increasingly focused on the role of technology in shaping, influencing, and extending our mental faculties. Technology extends the mind in two basic ways: through the creative design of artifacts and the purposive use of instruments. If the meaningful activity of technological artifacts were exhaustively described in these mind-dependent terms, then a philosophy of technology would depend entirely on our theory of mind. In this dissertation, I argue that a mind-dependent approach to technology is mistaken. Instead, (...) some machines are best understood as independent participants in their own right, contributing to and augmenting a variety of social practices as active, though often unrecognized, community members. Beginning with Turing’s call for “fair play for machines”, I trace an argument concerning the social autonomy of nonhuman agents through the artificial intelligence debates of the 20th century. I’ll argue that undue focus on the mind has obscured the force of Turing’s proposal, leaving the debates in an unfortunate stalemate. I will then examine a network theoretic alternative to the study of multi-agent complex systems that can avoid anthropocentric, mind-dependent ways of framing human-machine interactions. I argue that this approach allows for both scientific and philosophical treatment of large and complicated sociotechnical systems, and suggests novel methods for designing, managing, and maintaining such systems. Rethinking machines in mind-independent terms will illuminate the nature, scope, and evolution of our social and technological practices, and will help clarify the relationships between minds, machines, and the environments we share. (shrink)

The World Wide Web (Web) provides access to a global space of information assets and computational services. It also, however, serves as a platform for social interaction (e.g., Facebook) and participatory involvement in all manner of online tasks and activities (e.g., Wikipedia). There is a sense, therefore, that the advent of the Social Web has transformed our understanding of the Web. In addition to viewing the Web as a form of information repository, we are now able to (...) view the Web in more social terms. In particular, it has become possible to see the Web as providing access to the human social environment. This is important, because issues of social embedding and social interaction have been seen to contribute to the emergence of human cognitive capabilities. The ability of the Web to provide access to the human social environment thus raises an important question: Can humanity play a productive role in the emergence of advanced forms of machine intelligence by virtue of their interactions and engagements with the online realm? The present paper attempts to show why this question is worth asking. It also attempts to highlight some of the ways in which Web-based forms of contact with the human social environment may be relevant to research into machine intelligence. (shrink)

In this paper, we address the question of what current computers are from the point of view of human-computer interaction. In the early days of computing, the Turing machine (TM) has been the cornerstone of the understanding of computers. The TM defines what can be computed and how computation can be carried out. However, in the last decades, computers have evolved and increasingly become interactive systems, reacting in real-time to external events in an ongoing loop. We argue that the (...) TM does not provide a mechanistic explanation for interactive computing. The reason is that the fundamental phenomena relevant to interactive computing are out of the scope of classical computability theory. Part of the explanatory power of the TM relies on what we propose to call an execution model. An execution model belongs to a level of abstraction where it is possible to describe both the functional architecture and the execution in mechanistic terms. An updated execution model is warranted to provide the minimal mechanistic description for interactive computation as a counterpart of what the TM could explain regarding Church-Turing computation. It would support an explanation of the ubiquitous computing devices we know - those interacting with humans, e.g., through digital interfaces. We show that such a model is not available within interactive models of computation and that relevant abstractions and concerns are available in computer engineering but need to be identified and gathered. To fill this void, we propose to reflect on the level of abstraction required to support the mechanistic description of an interactive execution and propose some preliminary requirements. (shrink)

Social machines are systems formed by technical and human elements interacting in a structured manner. The use of digital platforms as mediators allows large numbers of human participants to join such mechanisms, creating systems where interconnected digital and human components operate as a single machine capable of highly sophisticated behaviour. Under certain conditions, such systems can be described as autonomous and goal-driven agents. Many examples of modern Artificial Intelligence (AI) can be regarded as instances of this class of mechanisms. (...) We argue that this type of autonomous social machines has provided a new paradigm for the design of intelligent systems marking a new phase in the field of AI. The consequences of this observation range from methodological, philosophical to ethical. On the one side, it emphasises the role of Human-Computer Interaction in the design of intelligent systems, while on the other side it draws attention to both the risks for a human being and those for a society relying on mechanisms that are not necessarily controllable. The difficulty by companies in regulating the spread of misinformation, as well as those by authorities to protect task-workers managed by a software infrastructure, could be just some of the effects of this technological paradigm. (shrink)

Robots and other smart machines are increasingly interwoven into the social fabric of our society, with the area and scope of their application continuing to expand. As we become accustomed to interacting through and with robots, we also begin to supplement or replace existing human–human interactions with human–machine interactions. This article aims to discuss the impacts of the shift from human–human interactions to human–machine interactions in one facet of our self-constitution, i.e., morality. More specifically, it sets out (...) to explore whether and how the shift to human–machine interactions can affect our moral cultivation. I shall structure the article around what Shannon Vallor calls technology-driven moral deskilling, i.e., the phenomenon of technology negatively affecting individual moral cultivation, and shall also attempt to offer a Confucian response to the problem. I first elaborate in detail Vallor’s idea of technology-driven moral deskilling. Next, I discuss three paradigms of virtue acquisition identified by Nancy E. Snow, i.e., the “folk” paradigm, the skill-and-expertise paradigm, and the Confucian paradigm, and show how the Confucian paradigm can help us to respond to technology-driven moral deskilling. Finally, I introduce the idea of Confucian rituals (li) and argue for the ritualizing of machines as an answer to technology-driven moral deskilling. (shrink)

New technological advancements have always changed the way society and human relationships work. New affordances created by technological tools inevitable modify and affect the way people interact with such tools, as well as with one another, and with the world within which this technology is embedded. -/- Technology, Users and Uses explores and discusses ethical issues around the use of technology and AI, by focusing on the way they affect individual, social and global interactions. The collection addresses topics including (...) social networks, public opinion, fake news and information warfare; digitalisation and datafication of society and individuals; and transhumanism, super-intelligent machines and the technological singularity. -/- Technology, Users and Uses aims to offer theoretical and practical guidelines and recommendations for regulators, developers, engineers and scientists, as well as for researchers, educators and scholars in the fields of technology and AI, philosophy and sociology. (shrink)

This article examines what the rise in machine learning systems might mean for social theory. Focusing on financial markets, in which algorithmic securities trading founded on ML-based decision-making is gaining traction, I discuss the extent to which established sociological notions remain relevant or demand a reconsideration when applied to an ML context. I argue that ML systems have some capacity for agency and for engaging in forms of collective machine behaviour, in which ML systems interact with other machines. However, (...) ML-based collective machine behaviour is irreducible to human decision-making and thereby challenges established sociological notions of financial markets. I argue that such behaviour can nonetheless be analysed through an adaptation of sociological theories of interaction and collective behaviour. (shrink)

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human–machine interactions, is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different (...) perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries.This paper advocates for establishing an interdisciplinary research domain—Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence and maintenance of collective intelligence and extend it to human–AI systems. We connect this with synergistic work on a compatible cognitive architecture, instance-based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human–machine intelligence. (shrink)

The paper reports our research aiming at utilization of human interactive communication modeling principles in application to a novel interaction paradigm designed for brain–computer/machine-interfacing (BCI/BMI) technologies as well as for socially aware intelligent environments or communication support systems. Automatic procedures for human affective responses or emotional states estimation are still a hot topic of contemporary research. We propose to utilize human brain and bodily physiological responses for affective/emotional as well as communicative interactivity estimation, which potentially could be used in (...) the future for human–machine/environment interaction design. As a test platform for such an intelligent human–machine communication application, an emotional stimuli paradigm was chosen to evaluate brain responses to various affective stimuli in an emotional empathy mode. Videos with moving faces expressing various emotional displays as well as speech stimuli with similarly emotionally articulated sentences are presented to the subjects in order to further analyze different affective responses. From information processing point of view, several challenges with multimodal signal conditioning and stimuli dynamic response extraction in time frequency domain are addressed. Emotions play an important role in human daily life and human-to-human communication. This is why involvement of affective stimuli principles to human–machine communication or machine-mediated communication with utilization of multichannel neurophysiological and periphery physiological signals monitoring techniques, allowing real-time subjective brain responses evaluation, is discussed. We present our preliminary results and discuss potential applications of brain/body affective responses estimation for future interactive/smart environments. (shrink)

A half-billion years ago, a few species of single-celled protozoa stumbled irreversibly from loose social interaction into a tight, specialized interdependence. They became multi-celled metazoa, and human beings are one sort. Metazoa greatly transcend their constituent cells in lifetime, abilities, experiences and even materials (like bone). New kind of beings emerged out of the interactions of the old.

The Internet provides access to a global space of information assets and computational services. It also, however, serves as a platform for social interaction (e.g., Facebook) and participatory involvement in all manner of online tasks and activities (e.g., Wikipedia). There is a sense, therefore, that the Internet yields an unprecedented form of access to the human social environment: it provides insight into the dynamics of human behavior (both individual and collective), and it additionally provides access to the (...) digital products of human cognitive labor (again, both individual and collective). Such access is interesting from the standpoint of research into machine intelligence, for the human social environment looks to be of crucial importance when it comes to the evolutionary and developmental origins of the human mind. In the present paper, we develop a theoretical account that sees the Internet as providing opportunities for online systems to function as socially-situated agents. The result is a vision of machine intelligence in which advanced forms of cognitive competence are seen to arise from the creation of a new kind of digital socio-ecological niche. The present paper attempts to detail this vision with respect to the notion of socially-scaffolded cognition. It also describes some of the forms of machine learning that may be required to enable online systems to press maximal cognitive benefit from their new-found informational contact with the human social world. (shrink)

A special area of human-machine interaction, the expression of emotions gains importance with the continuous development of artificial agents such as social robots or interactive mobile applications. We developed a prototype version of an abstract emotion visualization agent to express five basic emotions and a neutral state. In contrast to well-known symbolic characters (e.g., smileys) these displays follow general biological and ethological rules. We conducted a multiple questionnaire study on the assessment of the displays with Hungarian and Japanese (...) subjects. In most cases participants were successful in recognizing the displayed emotions. Fear and sadness were most easily confused with each other while both the Hungarian and Japanese participants recognized the anger display most correctly. We suggest that the implemented biological approach can be a viable complement to the emotion expressions of some artificial agents, for example mobile devices. (shrink)