In this chapter, we integrate three distinct strands of thought to argue that the concept of “fairness” varies significantly across cultures. As a result, ensuring that human-AI interactions meet relevant fairness standards requires a deep understanding of the cultural contexts in which AI-enabled systems are deployed. Failure to do so will not only result in the generation of unfair outcomes by an AI-enabled system, but it will also degrade legitimacy of and trust in the system. The first strand concerns (...) the dominant approach taken in the technology industry to ensure that AI-enabled systems are fair. This approach is to reduce fairness to some mathematical formalism that can be applied universally, a quintessentially Western conception of fairness. The second strand concerns alternative conceptions of fairness that have their roots in philosophical traditions of the East, namely Confucian virtue ethics. Understanding how individuals from diverse cultural backgrounds perceive fairness—particularly their beliefs about fairness in human-to-human interactions—is crucial for understanding how they will interpret fairness in human-AI interactions. Building on these philosophical and behavioral differences, as highlighted by empirical research, the third strand integrates insights from political science and interdisciplinary studies. This perspective offers valuable guidance on designing AI-enabled systems to align with contextually relevant standards of fairness. Examining existing beliefs about fairness within the context of institutional decision-making provides valuable insights into what people expect from AI-generated decisions. These expectations often include key elements such as a sufficient degree of transparency, clear lines of accountability, and mechanisms to contest decisions made by the system—all of which are essential components of procedural fairness. Rather than adopting a one-size-fits-all approach to ensuring fairness, the design and deployment of AI systems must carefully account for the operating environment, including the socio-political and cultural context, to ensure that the system aligns with the relevant standards of fairness. (shrink)

AI as decision support supposedly helps human agents make ‘better’decisions more efficiently. However, research shows that it can, sometimes greatly, influence the decisions of its human users. While there has been a fair amount of research on intended AI influence, there seem to be great gaps within both theoretical and practical studies concerning unintended AI influence. In this paper I aim to address some of these gaps, and hope to shed some light on the ethical and moral concerns (...) that arise with unintended AI influence. I argue that unintended AI influence has important implications for the way we perceive and evaluate human-AI interaction. To make this point approachable from both the theoretical and practical side, and to avoid anthropocentricallyladen ambiguities, I introduce the notion of decision points. Based on this, the main argument of this paper will be presented in two consecutive steps: i) unintended AI influence doesn’t allow for an appropriate determination of decision points-this will be introduced as decision-point-dilemma, and ii) this has important implications for the ascription of responsibility. (shrink)

The rapid integration of these artificial intelligence (AI) tools into our daily lives is reshaping how we think and make decisions. We propose that data-driven AI systems, by transcending individual artefacts and interfacing with a dynamic, multiartefact ecosystem, constitute a distinct psychological system. We call this ‘system 0’, and position it alongside Kahneman’s system 1 (fast, intuitive thinking) and system 2 (slow, analytical thinking).System 0 represents the outsourcing of certain cognitive tasks to AI, which can process vast amounts of data (...) and perform complex computations beyond human capabilities. It emerges from the interaction between users and AI systems, which creates a dynamic, personalized interface. Authors: Massimo Chiriatti, Marianna Ganapini, Enrico Panai, Mario Ubiali & Giuseppe Riva. (shrink)

As technology improves, artificial systems are increasingly able to behave in human-like ways: holding a conversation; providing information, advice, and support; or taking on the role of therapist, teacher, or counsellor. This enhanced behavioural complexity, we argue, encourages deeper forms of affective engagement on the part of the human user, with the artificial agent helping to stabilise, subdue, prolong, or intensify a person’s emotional condition. Here, we defend a fictionalist account of human/AI interaction, according to which (...) these encounters involve an elaborate practise of imaginative pretence: a make-believe in which the artificial agent is attributed a life of its own. We attend, specifically, to the temporal characteristics of these fictions, and to what we imagine artificial agents are doing when we are not looking at them. (shrink)

Volume 25, Issue 3, March 2025, Page 148-150.

Language is the foundational grammar of human life. As an evolutionary feat, the development of language in Homo Sapien societies meant an elaborate structure of intricate discourse formation through fiction and myths, which naturalises over time to build a system of culture. The narrativisation of fiction involves complex devices that require meticulous examination to understand cultural codes and their role in shaping collective ideology. Language as a means of interaction with non-human entities like Artificial Intelligence as depicted (...) in films, also plays a vital role in shaping the perception of the self and identity. Employing Deleuze and Guattari’s notion of “becoming” and the rhizomatic self, this paper will study the dialogues and narrativity of Jonze’s Her to critically analyse the fluid transformation of Theodore through his interaction with the operating system Samantha, reflecting on the broader implications for human-AI interaction and identity formation. (shrink)

The revolution of artificial intelligence (AI), particularly generative AI, and its implications for human–robot interaction (HRI) opened up the debate on crucial regulatory, business, societal, and ethical considerations. This paper explores essential issues from the anthropomorphic perspective, examining the complex interplay between humans and AI models in societal and corporate contexts. We provided a comprehensive review of existing literature on HRI, with a special emphasis on the impact of generative models such as ChatGPT. The scientometric study posits that (...) due to their advanced linguistic capabilities and ability to mimic human-like behavior, generative AIs like ChatGPT will continue to grow in popularity in pair with human rational empathy, tendency for personification and their advanced linguistic capabilities and ability to mimic human-like behavior. As they blur the boundaries between humans and robots, these models introduce fresh moral and philosophical dilemmas. Our research aims to extrapolate key trends and unique factors in HRI and to elucidate the technical aspects of generative AI that enhance its effectiveness in this field compared to traditional rule-based AI systems. We further discuss the challenges and limitations of applying generative AI in HRI, providing a future research agenda for AI optimization in diverse applications, including education, entertainment, and healthcare. (shrink)

The hypergrowth and hyperconnectivity of networks of artificial intelligence (AI) systems and algorithms increasingly cause our interactions with the world, socially and environmentally, more technologically mediated. AI systems start interfering with our choices or making decisions on our behalf: what we see, what we buy, which contents or foods we consume, where we travel to, who we hire, etc. It is imperative to understand the dynamics of human-computer interaction in the age of progressively more competent AI. This essay (...) presents five premises to serve as starting points for building our understanding of the “interactions’ in human-computer interactions. (shrink)

Philosophical and sociological approaches in technology have increasingly shifted toward describing AI (artificial intelligence) systems as ‘(moral) agents,’ while also attributing ‘agency’ to them. It is only in this way – so their principal argument goes – that the effects of technological components in a complex human-computer interaction can be understood sufficiently in phenomenological-descriptive and ethical-normative respects. By contrast, this article aims to demonstrate that an explanatory model only achieves a descriptively and normatively satisfactory result if the concepts (...) of ‘(moral) agent’ and ‘(moral) agency’ are exclusively related to human agents. Initially, the division between symbolic and sub-symbolic AI, the black box character of (deep) machine learning, and the complex relationship network in the provision and application of machine learning are outlined. Next, the ontological and action-theoretical basic assumptions of an ‘agency’ attribution regarding both the current teleology-naturalism debate and the explanatory model of actor network theory are examined. On this basis, the technical-philosophical approaches of Luciano Floridi, Deborah G. Johnson, and Peter-Paul Verbeek will all be critically discussed. Despite their different approaches, they tend to fully integrate computational behavior into their concept of ‘(moral) agency.’ By contrast, this essay recommends distinguishing conceptually between the different entities, causalities, and relationships in a human-computer interaction, arguing that this is the only way to do justice to both human responsibility and the moral significance and causality of computational behavior. (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)

A discussion concerning whether to conceive Artificial Intelligence systems as responsible moral entities, also known as “artificial moral agents”, has been going on for some time. In this regard, we argue that the notion of “moral agency” is to be attributed only to humans based on their autonomy and sentience, which AI systems lack. We analyze human responsibility in the presence of AI systems in terms of meaningful control and due diligence and argue against fully automated systems in medicine. (...) With this perspective in mind, we focus on the use of AI-based diagnostic systems and shed light on the complex networks of persons, organizations and artifacts that come to be when AI systems are designed, developed, and used in medicine. We then discuss relational criteria of judgment in support of the attribution of responsibility to humans when adverse events are caused or induced by errors in AI systems. (shrink)

Can AI solve the ethical, moral, and political dilemmas of warfare? How is artificial intelligence (AI)-enabled warfare changing the way we think about the ethical-political dilemmas and practice of war? This article explores the key elements of the ethical, moral, and political dilemmas of human-machine interactions in modern digitized warfare. It provides a counterpoint to the argument that AI “rational” efficiency can simultaneously offer a viable solution to human psychological and biological fallibility in combat while retaining “meaningful” (...) class='Hi'>human control over the war machine. This Panglossian assumption neglects the psychological features of human-machine interactions, the pace at which future AI-enabled conflict will be fought, and the complex and chaotic nature of modern war. The article expounds key psychological insights of human-machine interactions to elucidate how AI shapes our capacity to think about future warfare's political and ethical dilemmas. It argues that through the psychological process of human-machine integration, AI will not merely force-multiply existing advanced weaponry but will become de facto strategic actors in warfare – the “AI commander problem.”. (shrink)

This book serves as a guide to understanding the dynamics of AI in human contexts with a specific focus on the generation, sharing, and consumption of misinformation online. How do humans and AI interact? How is AI shaping our understanding of ourselves and our societies? What are the interaction mechanisms that govern how humans and algorithms contribute to misinformation online? And how do we bridge the gap between ethical considerations and practical realities to make responsible, reliable systems? Exploring (...) these questions, the book empowers humans to make AI design choices that allow them meaningful control over AI and the online sphere. Calling for an interdisciplinary approach toward human-misinformation algorithmic interaction that focuses on building methods and tools that robustly deal with complex psychological/social phenomena, the book offers a compelling insight into the future of AI-based society. (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)

Real engines of the artificial intelligence revolution, machine learning models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In (...) this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs. (shrink)

Psychological attitudes towards service and personal robots are selectively examined from the vantage point of psychoanalysis. Significant case studies include the uncanny valley effect, brain-actuated robots evoking magic mental powers, parental attitudes towards robotic children, idealizations of robotic soldiers, persecutory fantasies involving robotic components and systems. Freudian theories of narcissism, animism, infantile complexes, ego ideal, and ideal ego are brought to bear on the interpretation of these various items. The horizons of Human-robot Interaction are found to afford new (...) and fertile grounds for psychoanalytic theorizing beyond strictly therapeutic contexts. (shrink)

In response to the ever-increasing spread of online disinformation and misinformation, several human–computer interaction tools to enhance data literacy have been developed. Among them, many employ elements of gamification to increase user engagement and reach out to a broader audience. However, there are no systematic criteria to analyze their relevance and impact for building fake news resilience, partly due to the lack of a common understanding of data literacy. In this paper we put forward an operationalizable definition of (...) data literacy as a form of multidimensional critical thinking. We then survey 22 existing tools and classify them according to a framework of 10 criteria pointing to their gameful design and educational features. Through a comparative/contrastive analysis informed by a focus group, we provide a principled set of guidelines to develop more efficient human–computer interaction tools to teach how to critically think in the current media ecosystem. (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)

This article advocates for a hermeneutic model for children-AI (age group 7–11 years) interactions in which the desirable purpose of children’s interaction with artificial intelligence (AI) systems is children's growth. The article perceives AI systems with machine-learning components as having a recursive element when interacting with children. They can learn from an encounter with children and incorporate data from interaction, not only from prior programming. Given the purpose of growth and this recursive element of AI, the article argues (...) for distinguishing the interpretation of bias within the artificial intelligence (AI) ethics and responsible AI discourse. Interpreting bias as a preference and distinguishing between positive (pro-diversity) and negative (discriminative) bias is needed as this would serve children's healthy psychological and moral development. The human-centric AI discourse advocates for an alignment of capacities of humans and capabilities of machines by a focus both on the purpose of humans and on the purpose of machines for humans. The emphasis on mitigating negative biases through data protection, AI law, and certain value-sensitive design frameworks demonstrates that the purpose of the machine for humans is prioritized over the purpose of humans. These top–down frameworks often narrow down the purpose of machines to do-no-harm and they miss accounting for the bottom-up views and developmental needs of children. Therefore, applying a growth model for children-AI interactions that incorporates learning from negative AI-mediated biases and amplifying positive ones would positively benefit children’s development and children-centric AI innovation. Consequently, the article explores:What challenges arise from mitigating negative biases and amplifying positive biases in children-AI interactions and how can a growth model address these?To answer this, the article recommends applying a growth model in open AI co-creational spaces with and for children. In such spaces human–machine and human–human value alignment methods can be collectively applied in such a manner that children can (1) become sensitized toward the effects of AI-mediated negative biases on themselves and others; (2) enable children to appropriate and imbue top-down values of diversity, and non-discrimination with their meanings; (3) enforce children’s right to identity and non-discrimination; (4) guide children in developing an inclusive mindset; (5) inform top-down normative AI frameworks by children’s bottom-up views; (6) contribute to design criteria for children-centric AI. Applying such methods under a growth model in AI co-creational spaces with children could yield an inclusive co-evolution between responsible young humans in the loop and children-centric AI systems. (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)

In this article, we suggest that the study of social interactions and the development of a “sense of agency” in joint action can help determine the content of relevant explanations to be implemented in artificial systems to make them “explainable.” The introduction of automated systems, and more broadly of Artificial Intelligence, into many domains has profoundly changed the nature of human activity, as well as the subjective experience that agents have of their own actions and their consequences – an (...) experience that is commonly referred to as sense of agency. We propose to examine the empirical evidence supporting this impact of automation on individuals’ sense of agency, and hence on measures as diverse as operator performance, system explicability and acceptability. Because of some of its key characteristics, AI occupies a special status in the artificial systems landscape. We suggest that this status prompts us to reconsider human–AI interactions in the light of human–human relations. We approach the study of joint actions in human social interactions to deduce what key features are necessary for the development of a reliable sense of agency in a social context and suggest that such framework can help define what constitutes a good explanation. Finally, we propose possible directions to improve human–AI interactions and, in particular, to restore the sense of agency of human operators, improve their confidence in the decisions made by artificial agents, and increase the acceptability of such agents. (shrink)

Negative attitudes toward robots are considered as one of the psychological factors preventing humans from interacting with robots in the daily life. To verify their influence on humans‘ behaviors toward robots, we designed and executed experiments where subjects interacted with Robovie, which is being developed as a platform for research on the possibility of communication robots. This paper reports and discusses the results of these experiments on correlation between subjects’ negative attitudes and their behaviors toward robots. Moreover, it discusses influences (...) of gender and experience of real robots on their negative attitudes and behaviors toward robots. (shrink)

Presence is a palpable sense of space, things and others that overlaps with matters of meaning, yet is not reducible to it: it is a dimension of things that hides in plain sight. This paper is motivated by observations that (1) presence is under-appreciated in questions of modern and nascent human-synthetic agent interaction, and (2) that presence matters because it affects and moves us. The paper’s goal is to articulate a multi-faceted understanding of presence, and why it matters, (...) so the importance of presence may be readily understood by those who regulate media, digital and artificial intelligence (AI) industries. Novel forms of presence raise all sorts of questions of what it means to live with new forms of presences. Some of these are highly positive and others are resistant to simplistic moral diagnosis, a point explored through extended consideration of ‘thanatechnology’ and ‘ghostbots’. To clarify and foreground presence, this paper draws on continental philosophy and technologist ideas about presence to understand the significance and parameters of presence. It then puts these to work by considering a range of existing and emerging human-synthetic agent interactions, arguing that that presence is an underappreciated yet crucial factor in human-synthetic interactions, particularly involving AI and ghostbots. The paper concludes with points of focus for organisations charged with media, data protection and AI governance regarding facets of presence-based characteristics for emergent human-synthetic interaction. Foremost is consideration of open standards for a presence-based afterlife and suggestion of a temporal firewall, or a break of time before resurrection, to prevent harm to those who have recently lost someone. (shrink)

Just as AI has moved away from classical AI, human-computer interaction (HCI) must move away from what I call ‘good old fashioned HCI’ to ‘new HCI’ – it must become a part of cognitive systems research where HCI is one case of the interaction of intelligent agents (we now know that interaction is essential for intelligent agents anyway). For such interaction, we cannot just ‘analyze the data’, but we must assume intentions in the other, and (...) I suggest these are largely recognized through resistance to carrying out one’s own intentions. This does not require fully cognitive agents but can start at a very basic level. New HCI integrates into cognitive systems research and designs intentional systems that provide resistance to the human agent. (shrink)

Time and technology permeate the fabric of teamwork across a variety of settings to affect outcomes which have a wide range of consequences. However, there is a limited understanding about the interplay between these factors for teams, especially as applied to artificial intelligence (AI) technology. With the increasing integration of AI into human teams, we need to understand how environmental factors such as time scarcity interact with AI technology to affect team behaviors. To address this gap in the literature, (...) we investigated the interaction between the availability of intelligent technology and time scarcity in teams. Drawing from the theoretical perspective of computers are social actors and extant research on the use of heuristics and human–AI interaction, this study uses behavioral data from 56 teams who participated in a between-subjects 2 (intelligent assistant available × control/no intelligent assistant) × 2 (time scarcity × control/no time scarcity) lab experiment. Results show that teams working under time scarcity used the intelligent assistant more often and underperformed on a creative task compared to teams without the temporal constraints. Further, teams who had an intelligent assistant available to them had fewer interactions between members compared to teams who did not have the technology. Implications for research and applications are discussed. (shrink)

In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial (...) intelligence image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human–computer interaction in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce. (shrink)

To address the tension between demands for more transparent AI systems and the aim to develop and design robots with apparent agency for smooth and intuitive human-robot interaction (HRI), we present in this paper an argument for why explainability in HRI would benefit from being question-centered. First, we review how explainability has been discussed in AI and HRI respectively, to then present the challenge in HRI to accommodate the requirement of transparency while also keeping up the appearance of (...) the robot as a social agent. Based on the conceptualization of questions as information-seeking acts, our proposal for a question-centered approach to explainable HRI (xHRI) implies that to ensure transparency, robots shall provide explanations upon request in the form of questioning. We end this paper by laying out a preliminary taxonomy of xHRI-relevant questions that present the kinds of explanations a robot can or should be able to provide for successful and transparent HRI. (shrink)

This article develops a critical theory of human–computer interaction intended to test some of the assumptions and omissions made in the field as it transitions from a cognitive theoretical frame to a phenomenological understanding of user experience described by Harrison et al. as a third research paradigm and similarly Bødker :24–31; Bødker, Interactions 22):24–31, 2015) as third-wave HCI. Although this particular focus on experience has provided some novel avenues of academic enquiry, this article draws attention to a distinct (...) bridge between the conventional HCI disciplinary concerns with predominantly task-based digital work and use context and a growing business interest in consumer experiences in digital environments. Critical HCI addresses the problem of experience in two interrelated ways. On one hand, it explores the role market logic plays in putting user experiences to work. On the other hand, it engages with ontological understandings of experience hitherto realized in HCI by way of a phenomenological matrix. The article concludes by bringing in an old thinker to consider experience in novel ways that relate ontological concerns to a broader political concept of experience capitalism. (shrink)

Using anthropological theory, this paper examines human–android interactions (HAI) as an emerging aspect of android science. These interactions are described in terms of adaptive learning (which is largely subconscious). This article is based on the observations reported and supplementary data from two studies that took place in Japan with a teleoperated android robot called Telenoid in the socialization of school children and older adults. We argue that interacting with androids brings about a special context, an interval, and a space/time (...) for reflection and imagination that was not there before. During the interaction something happens. There is adaptive learning and as a result, both children and older adults accepted Telenoid, and the children and older adults accepted each other. Using frames of play and ritual, we make sense and ‘capture’ moments of adaptive learning, and the feedback that elicits a social response from all study participants that results in self-efficacy and socialization. While “ritual” refers to the application of what has been learned and “play” means that there are no obvious consequences of what has been learned. This analysis illuminates new understanding about the uncanny valley, cultural robotics and the therapeutic potential of HAI. This has implications for the acceptance of androids in ‘socialized roles’ and gives us insight into the subconscious adaptive learning processes that must take place within humans to accept androids into our society. This approach aims to provides a clearer conceptual basis and vocabulary for further research of android and humanoid development. (shrink)

The advent of the so-called Companion Robots is raising many ethical concerns among scholars and in the public opinion. Focusing mainly on robots caring for the elderly, in this paper we analyze these concerns to distinguish which are directly ascribable to robotic, and which are instead preexistent. One of these is the “deception objection”, namely the ethical unacceptability of deceiving the user about the simulated nature of the robot’s behaviors. We argue on the inconsistency of this charge, as today formulated. (...) After that, we underline the risk, for human-robot interaction, to become a hallucinatory relation where the human would subjectify the robot in a dynamic of meaning-overload. Finally, we analyze the definition of “quasi-other” relating to the notion of “uncanny”. The goal of this paper is to argue that the main concern about Companion Robots is the simulation of a human-like interaction in the absence of an autonomous robotic horizon of meaning. In addition, that absence could lead the human to build a hallucinatory reality based on the relation with the robot. (shrink)

An important tradition in philosophy holds that in order to successfully perform a joint action, the participants must be capable of establishing commitments on joint goals and shared plans. This suggests that social robotics should endow robots with similar competences for commitment management in order to achieve the objective of performing joint tasks in human-robot interactions. In this paper, we examine two philosophical approaches to commitments. These approaches, we argue, emphasize different behavioral and cognitive aspects of commitments that give (...) roboticists a way to give meaning to monitoring and pro-active signaling in joint action with human partners. To show that, we present an example of use-case with guiding robots and we sketch a framework that can be used to explore the type of capacities and behaviors that a robot may need to manage commitments. (shrink)

This article critically examines the paradigm shift from the attention economy to the intimacy economy—a market system where personal and emotional data are exchanged for customized experiences that cater to individual emotional and psychological needs. It explores how AI transforms these personal and emotional inputs into services, thereby raising essential questions about the authenticity of digital interactions and the potential commodification of intimate experiences. The study delineates the roles of human–computer interaction and AI in deepening personal connections, significantly (...) impacting emotional dynamics, and underscores AI’s role in various applications, from healthcare to grief tech, highlighting both enhancements in emotional connections and potential disruptions to genuine human interactions. An AI-mediated framework (AMIE) is introduced to assess how AI reshapes these connections and the overall digital society through personalized interactions. This framework explores the interplay between human emotions and AI-generated responses within the new Avatar Sphere, emphasizing the necessity for regulatory measures to safeguard digital identities, recognize emotional data as intellectual property, and maintain system transparency. It highlights the critical need for maintaining genuine human interactions and advocates for context-aware consent, continuous monitoring, and cross-cultural considerations to foster ethical AI practices. Leveraging blockchain technology and decentralized autonomous organizations, the framework proposes methods to enhance individual control over emotional data, mitigating the commodification risks. The findings contribute to ongoing discussions on AI ethics, digital privacy, and the future of human-AI interactions, providing valuable insights for cultivating a responsible intimacy economy. (shrink)

People meeting a robot for the first time do not know what it is capable of and therefore how to interact with it—what actions to produce, and how to produce them. Despite social robotics’ long-standing interest in the effects of robots’ appearance and conduct on users, and efforts to identify factors likely to improve human–robot interaction, little attention has been paid to how participants evaluate their robotic partner in the unfolding of actual interactions. This paper draws from qualitative (...) analyses of video-recorded interactions between a robot and groups of participants, in the framework of ethnomethodology and conversation analysis. We analyse the particular ways in which participants shape their embodied actions, how they can reproduce a prior action that failed to obtain a response from the robot; and how they explore the robot’s embodied nature. We find a set of recurrent methods or practices, showing that robot-recipient design displays not only participants’ initial assumptions about the robot’s competences, but also more importantly perhaps their continuous assessment of the robot’s behaviour, and their attempts to adapt to it. Participants locally produce and constantly revise their understanding of the robot as a more or less competent co-participant, drawing from its past, current, and projected conduct and responsiveness. We discuss the implications of these findings for research in robotics and human–robot interactions, and the value of the approach to shed new light on old questions by paying attention to the quality of gesture and the sequential organisation of interaction. (shrink)

Robotization and artificial intelligence are expected to change societies profoundly. Trust is an important factor of human–technology interactions, as robots and AI increasingly contribute to tasks previously handled by humans. Currently, there is a need for studies investigating trust toward AI and robots, especially in first-encounter meetings. This article reports findings from a study investigating trust toward robots and AI in an online trust game experiment. The trust game manipulated the hypothetical opponents that were described as either AI or (...) robots. These were compared with control group opponents using only a human name or a nickname. Participants lived in the United States. Describing opponents with robots or AI did not impact participants’ trust toward them. The robot called jdrx894 was the most trusted opponent. Opponents named “jdrx894” were trusted more than opponents called “Michael.” Further analysis showed that having a degree in technology or engineering, exposure to robots online and robot use self-efficacy predicted higher trust toward robots and AI. Out of Big Five personality characteristics, openness to experience predicted higher trust, and conscientiousness predicted lower trust. Results suggest trust on robots and AI is contextual and it is also dependent on individual differences and knowledge on technology. (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)

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)

This paper argues that there is a need to critically assess bias in the representations of older adults in the field of Human–Robot Interaction. This need stems from the recognition that technology development is a socially constructed process that has the potential to reinforce problematic understandings of older adults. Based on a qualitative content analysis of 96 academic publications, this paper indicates that older adults are represented as; frail by default, independent by effort; silent and technologically illiterate; burdensome; (...) and problematic for society. Within these documents, few counternarratives are present that do not take such essentialist representations. In these texts, the goal of social robots in elder care is to “enable” older adults to “better” themselves. The older body is seen as “fixable” with social robots, reinforcing an ageist and neoliberal narrative: older adults are reduced to potential care receivers in ways that shift care responsibilities away from the welfare state onto the individual. (shrink)

To integrate social robots in real-life contexts, it is crucial that they are accepted by the users. Acceptance is not only related to the functionality of the robot but also strongly depends on how the user experiences the interaction. Established design principles from usability and user experience research can be applied to the realm of human–robot interaction, to design robot behavior for the comfort and well-being of the user. Focusing the design on these aspects alone, however, comes (...) with certain ethical challenges, especially regarding the user’s privacy and autonomy. Based on an example scenario of human–robot interaction in elder care, this paper discusses how established design principles can be used in social robotic design. It then juxtaposes these with ethical considerations such as privacy and user autonomy. Combining user experience and ethical perspectives, we propose adjustments to the original design principles and canvass our own design recommendations for a positive and ethically acceptable social human–robot interaction design. In doing so, we show that positive user experience and ethical design may be sometimes at odds, but can be reconciled in many cases, if designers are willing to adjust and amend time-tested design principles. (shrink)

Human-computer interaction today has got a touch of magic: Without understanding the causal coherence, using a computer seems to become the art to use the right spell with the mouse as the magic wand — the sorcerer's staff. Goethes's poem admits an allegoric interpretation. We explicate the analogy between using a computer and casting a spell with emphasis on teaching magic skills. The art to create an ergonomic user interface has to take care of various levels of skills (...) for the human operators. The problem of logical discontinuities as opposed to continuous control is the most serious obstacle for human-computer interaction. (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)

With recent major progress in AI, AI-driven robots are expected to operate outside of their original settings, such as factory floors, and be integrated into our daily lives. In this trajectory, integrating culture, with its broad and vague meaning, into human–robot interaction becomes a concern for the field of social robotics. In this article, we propose a culturally-responsive robot as a concept that encapsulates critical theoretical and experimental studies of culture in robotics without subscribing to the dominant view (...) of the field that translates culture only to nationality. The main contribution of this article is to propose a methodology for implementing culturally-responsive robots, termed “robot-ethnographers.” Drawing from the long history of conventional ethnography, we define a robot-ethnographer as a robot situated within interaction contexts, shaping interactions, and learning culture through observation to adapt its behaviours accordingly. The paper analyses the properties of the robot-ethnographer in line with those of conventional ethnography in social science. Whilst acknowledging important technological limitations, this article concludes that robot-ethnographers can create culturally-responsive human–robot interactions. (shrink)

In order to improve text reading ability, a human-computer interaction method based on artificial intelligence human-computer interaction is proposed. Firstly, the design of the AI human-computer interaction model is constructed, which includes the Stanford Question Answering Dataset and the designed baseline model. There are three components: the coding layer is based on a cyclic neural network, which aims to encode the problem and text into a hidden state; the interaction layer is used to (...) integrate problems and text representation; the output layer connects two independent soft Max layers after a fully connected layer, one is used to obtain the starting position of the answer in the text and the other is used to obtain the ending position. In the interaction layer of the model, this manuscript uses hierarchical attention and aggregation mechanism to improve text coding. The traditional model interaction layer has a simple structure, which leads to weak relevance between text and problems, and poor understanding ability of the model. Finally, the self-attention model is used to further enhance the feature representation of text. The experimental results show that the improved model in this manuscript is compared with the public AI human-computer interaction reading comprehension model. According to the data in the table, the accuracy of the model in this manuscript is better than that of the baseline model, in which the exact match value is increased by 1.4% and the F1 value is increased by 2.7%. However, compared with improvement point 2, the EM and F1 values of the model have decreased by 0.7%. It shows that the output layer has a certain impact on the effect of the model, and the improvement and optimization of the output layer can also improve the performance of the model. It is proved that AI human-computer interaction can effectively improve text reading ability. (shrink)

Bringing human–robot interaction (HRI) into conversation with scholarship from human geography, this paper considers how socially interactive robots become important agents in the production of social space and explores the utility of core geographic concepts of _scale_ and _place_ to critically examine evolving robotic spatialities. The paper grounds this discussion through reflections on a collaborative, interdisciplinary research project studying the development and deployment of interactive museum tour-guiding robots on a North American university campus. The project is a (...) collaboration among geographers, roboticists, a digital artist, and the directors/curators of two museums, and involves experimentation in the development of a tour-guiding robot with a “socially aware navigation system” alongside ongoing critical reflection into the socio-spatial context of human–robot interactions and their future possibilities. The paper reflects on the tensions between logics of control and contingency in robotic spatiality and argues that concepts of scale and place can help reflect on this tension in a productive way while calling attention to a broader range of stakeholders who should be included in robotic design and deployment. (shrink)

Mark Coeckelbergh (Int J Soc Robot 1:217–221, 2009) argues that robot ethics should investigate what interaction with robots can do to humans rather than focusing on the robot’s moral status. We should ask what robots do to our sociality and whether human–robot interaction can contribute to the human good and human flourishing. This paper extends Coeckelbergh’s call and investigate what it means to live with disembodied AI-powered agents. We address the following question: Can the (...) class='Hi'>human–AI interaction contribute to our moral development? We present an empirically informed philosophical analysis of how the AI personal assistant Siri changes its users’ way of life, based on the responses obtained from 20 semi-directive individual interviews with Siri users. We identify changes in the users’ social interaction associated with the adoption of Siri. These changes include: (1) the indirect effect of reducing opportunities of human interaction, (2) the second-order effect of diminished expectations toward each other in a community, and (3) the acquired preference to obtain hassle-free interaction with Siri over human interaction. We examine them in relation to concerns that are voiced in the current debates over the rise of AI, namely the suspicion that humans could become overly reliant on AI (Danaher 2019) and the worry that social AI could impede on moral development (Fröding and Peterson, Ethics Inf Technol 23:207–214, 2012; Li, Ethics Inf Technol 23:543–550, 2021). We analyze the ethical costs that come from these changes in light of virtue ethics and address potential objections along the way. We end by offering directions for thinking about how to live with AI personal assistant while preserving favorable conditions for moral development. (shrink)

The advent of AI calls for an existential self-redefinition of humanity. It necessitates the establishment of a pluralistic global humanist culture that enables us to coexist in the new world of active media and autopoietic technology. In this paper, related philosophical questions give rise to the proposal of a novel metaculture that elevates human heritages and cultural memories to the plane of a digital AI- based infrastructure. I argue for a balanced and holistic approach to human-to-human and (...) human–AI interactions and metaculture that can be developed in the form of combinatorial “metagames” that reflect the potential of human consciousness, transcend the boundaries between technology, human embodiment and biology, the arts, sciences and humanities, and philosophy, and, finally, include the element of meaningful coincidence and spontaneity “in the second potency.” Hermann Hesse’s final novel, The Glass Bead Game, offers a key to understanding this basic idea. His vision is extremely relevant today. Based on this, the concept of cultivating global cultural memories through innovative aesthetic-epistemic “Glass Bead Games 2.0” is discussed further from a systematic angle. To complement this, reference is also made to Hesse’s own source of inspiration: Novalis’s “encyclopedist” approach interlinks aesthetic and epistemic productivity in a way that could become a major starting point to create a new holistic metaculture in the Age of AI. (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)