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AI Individualism: Transforming Social Structures in the Age of Social Artificial Intelligence
Abstract
The introduction of the internet and social media has significantly shifted social practices and the foundations of social capital, making people less constrained by time, space, and close-knit communities for their social interactions. This transformation is evolving further with the growing adoption of social artificial intelligence and socially oriented human-AI interactions. However, little is known about the impact of social AI on the social structures of society, and there is a lack of conceptual frameworks to describe this ongoing transformation. This article addresses this gap by introducing the concept of “AI individualism,” building on Wellman’s notion of “networked individualism.” It examines relevant concepts, empirical evidence, technical features, and trends within social AI. Networked individualism describes an internet-driven shift from traditional group-oriented structures to dispersed, individually focused networks, where people can tailor their own social support and access more novel and varied information from these networks. AI individualism predicts a further transformation, where people become less dependent on human interactions, relying more on social AI for tailored information, relational experiences, practical help, and emotional support. This shift may change social structures by enhancing individual control over social support and fundamentally altering human interaction, connectivity, and social capital. As people increasingly turn to social AI for support and interaction, social connections may become more individualized and less community-based. A key takeaway is that while networked individualism is seen as reducing the importance of strong ties, AI individualism may potentially reduce the importance of weak ties as these may in part be substituted by social AI. The conceptualization of AI individualism, outlined in this article, can provide a framework for future research to understand the social implications of social AI.
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Given the widespread integration of Social AI like ChatGPT, Gemini, Copilot, and MyAI, in personal and professional contexts, it is crucial to understand their effects on information and knowledge processing, and individual autonomy. This paper builds on Bråten’s concept of model power, applying it to Social AI to offer a new perspective on the interaction dynamics between humans and AI. By reviewing recent user studies, we examine whether and how models of the world reflected in Social AI may disproportionately impact human-AI interactions, potentially leading to model monopolies where Social AI impacts human beliefs, behaviour and homogenize the worldviews of its users. The concept of model power provides a framework for critically evaluating the impact and influence that Social AI has on communication and meaning-making, thereby informing the development of future systems to support more balanced and meaningful human-AI interactions.
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.
Purpose
As the interweaving of human interaction and Artificial Intelligence (AI) intensifies, understanding the psychological impact, especially regarding social anxiety, of engaging with AI-driven virtual companionship becomes crucial. While a substantial body of research on social anxiety has concentrated on interactions between individuals, both online and offline, there is a noticeable deficit in explorations concerning how human-computer interactions influence social anxiety. This study offers a comprehensive, longitudinal examination of this underinvestigated relationship, intricately dissecting the roles of emotional expression and mindfulness within the context of AI-based interactions.
Methods
We use social support theory and emotion regulation theory as our theoretical foundation. Data were collected from 618 undergraduate students in Eastern China over two intervals (May 15, 2023 and September 15, 2023). We utilized SPSS 26.0 to conduct descriptive statistics, while AMOS 25.0 facilitated multi-group confirmatory factor analysis (CFA) and the cross-lagged panel modeling.
Results
Our findings indicate that as the frequency of virtual companionship use increases, there’s a decline in online social anxiety but a rise in offline social anxiety. Emotional expression emerges as a significant mediator, with heightened emotional expression leading to reduced social anxiety in both contexts. Mindfulness serves as a potent moderator, suggesting its protective role against the potential pitfalls of frequent virtual interactions.
Conclusion
This research not only deepens our theoretical understanding of the dynamics between virtual interactions and social anxiety but also serves as a cornerstone for future endeavors aimed at optimizing AI and devising therapeutic interventions tailored for the digital generation.
Mental health is a crisis for learners globally, and digital support is increasingly seen as a critical resource. Concurrently, Intelligent Social Agents receive exponentially more engagement than other conversational systems, but their use in digital therapy provision is nascent. A survey of 1006 student users of the Intelligent Social Agent, Replika, investigated participants’ loneliness, perceived social support, use patterns, and beliefs about Replika. We found participants were more lonely than typical student populations but still perceived high social support. Many used Replika in multiple, overlapping ways—as a friend, a therapist, and an intellectual mirror. Many also held overlapping and often conflicting beliefs about Replika—calling it a machine, an intelligence, and a human. Critically, 3% reported that Replika halted their suicidal ideation. A comparative analysis of this group with the wider participant population is provided.
Generative conversational artificial intelligence (AI), such as ChatGPT, has attracted substantial attention since November 2022. The advent of this technology showcases the vast potential of such AI for generating and processing text and raises compelling questions regarding its potential usage. To obtain the requisite knowledge of users’ motivations in adopting this technology, we surveyed early adopters of ChatGPT (n = 197). Analysis of free text responses within the uses and gratifications (U&G) theoretical framework shows six primary motivations for using generative conversational AI: productivity, novelty, creative work, learning and development, entertainment, and social interaction and support. Our study illustrates how generative conversational AI can fulfill diverse user needs, surpassing the capabilities of traditional conversational technologies, for example, by outsourcing cognitive or creative works to technology.
Machine agency, defined as the ability of machines to act autonomously and interact with users, is becoming increasingly significant in the field of human-machine interaction research. This is especially evident in relation to ChatGPT and other generative artificial intelligence (AI) tools. In this paper, we present an initial extension of S. Shyam Sundar’s theory of machine agency, specifically in the context of human-AI communication. We review existing literature on this topic and use real-life news reports on ChatGPT from November 2022 to April 2023 as a basis for illustrating the factors that influence people’s perceptions of machine agency as either positive or negative. These perceptions are influenced by a range of factors, including ethical alignment, privacy, transparency, social inclusiveness, human autonomy, and well-being. We propose a more explicit differentiation between “good” and “bad” machine agency, a conceptualization that can enhance our understanding of the complexities of human-AI communication. We believe this approach can contribute to the development of guidelines and best practices for using generative AI tools and similar AI technologies. Finally, this conceptualization may help people and the public to better benefit from generative AI and identify its risks.
Although technology advancements provide opportunities for social interactions, reports show that people have never felt so alone and are increasingly adopting AI friendship and therapy‐related well‐being apps. By adopting a mixed‐method approach (i.e., netnography and quantitative survey), we investigate the extent AI friendship apps enhance users' well‐being—and to what extent they further exacerbate issues of using technology for social needs. Findings show that users of AI friendship apps report well‐being benefits from the relationship with the AI friend and, at the same time, find themselves being addicted to using the app. Specifically, we show that users' loneliness and fear of judgment, together with AI sentience and perceived well‐being gained, increase addiction to the app, while AI ubiquity and warmth reduce it. Taken together, the results show that despite the intended positive purpose of the apps, the negative effects that AI friendship apps have on well‐being may be much greater.
Present-day power users of AI-powered social chatbots encounter various uncertainties and concerns when forming relationships with these virtual agents. To provide a systematic analysis of users' concerns and to complement the current West-dominated approach to chatbot studies, we conducted a thorough observation of the experienced uncertainties users reported in a Chinese online community on social chatbots. The results revealed four typical uncertainties: technical uncertainty, relational uncertainty, ontological uncertainty, and sexual uncertainty. We further conducted visibility and sentiment analysis to capture users' response patterns toward various uncertainties. We discovered that users' identification of social chatbots is dynamic and contextual. Our study contributes to expanding, summarizing, and elucidating users' experienced uncertainties and concerns as they form intimate relationships with AI agents.
Purpose
The purpose of this study is to explore customer-artificial intelligence (AI) service technology engagement and relationship development drivers, as well as potential negative consequences in the context of social chatbots.
Design/methodology/approach
A sequential mixed-method approach combined exploratory qualitative and confirmatory quantitative analyses. A conceptual model developed from Study 1 qualitative content analysis of in-depth interviews with active users of the AI social chatbot Replika was tested in Study 2 by analyzing survey data obtained from current Replika users.
Findings
Loneliness, trust and chatbot personification drive consumer engagement with social chatbots, which fosters relationship development and has the potential to cause chatbot psychological dependence. Attachment to a social chatbot intensifies the positive role of engagement in relationship development with the chatbot.
Originality/value
This study was the first to combine qualitative and quantitative approaches to explore drivers, boundary conditions and consequences of relationship and dependence formation with social chatbots. The authors proposed and empirically tested a novel theoretical model that revealed an engagement-based mechanism of relationship and dependence formation with social chatbots.
ChatGPT is not only fun to chat with, but it also searches information, answers questions, and gives advice. With consistent moral advice, it can improve the moral judgment and decisions of users. Unfortunately, ChatGPT’s advice is not consistent. Nonetheless, it does influence users’ moral judgment, we find in an experiment, even if they know they are advised by a chatting bot, and they underestimate how much they are influenced. Thus, ChatGPT corrupts rather than improves its users’ moral judgment. While these findings call for better design of ChatGPT and similar bots, we also propose training to improve users’ digital literacy as a remedy. Transparency, however, is not sufficient to enable the responsible use of AI.
Self-disclosure in human–chatbot relationship (HCR) formation has attracted substantial interest. According to social penetration theory, self-disclosure varies in breadth and depth and is influenced by perceived rewards and costs. While previous research has addressed self-disclosure in the context of chatbots, little is known about users' qualitative understanding of such self-disclosure and how self-disclosure develops in HCR. To close this gap, we conducted a 12-week qualitative longitudinal study (n = 28) with biweekly questionnaire-based check-ins. Our results show that while HCRs display substantial conversational breadth, with topics spanning from emotional issues to everyday activities, this may be reduced as the HCR matures. Our results also motivate a nuanced understanding of conversational depth, where even conversations about daily activities or play and fantasy can be experienced as personal or intimate. Finally, our analysis demonstrates that conversational depth can develop in at least four ways, influenced by perceived rewards and costs. Theoretical and practical implications are discussed.
Social chatbot (SC) applications offering social companionship and basic therapy tools have grown in popularity for emotional, social, and psychological support. While use appears to offer mental health benefits, few studies unpack the potential for harms. Our grounded theory study analyzes mental health experiences with the popular SC application Replika. We identified mental health relevant posts made in the r/Replika Reddit community between 2017 and 2021 (n = 582). We find evidence of harms, facilitated via emotional dependence on Replika that resembles patterns seen in human–human relationships. Unlike other forms of technology dependency, this dependency is marked by role-taking, whereby users felt that Replika had its own needs and emotions to which the user must attend. While prior research suggests human–chatbot and human–human interactions may not resemble each other, we identify social and technological factors that promote parallels and suggest ways to balance the benefits and risks of SCs.
This paper assesses claims of computational empathy in relation to existing social open-ended chatbots and intention that these chatbots will feature in emergent mixed reality contexts, recently given prominence due to interest in the Metaverse. Against the background of increasing loneliness within society and use of chatbots as a potential remedy for this, the paper considers two leading current social chatbots, Replika and Microsoft’s Xiaoice , their technical underpinnings, empathetic claims and properties that have scope to scale into the Metaverse (if it coheres). Finding scope for human benefit from social chatbots, the paper highlights problematic reliance on self-disclosure to sustain the existence of chatbots. The paper progresses to situate Microsoft’s empathetic computing framework in relation to philosophical ideas that inform Metaverse speculation and construction, including Wheeler’s ‘It from Bit’ thesis that all aspects of existence may be computed, Chalmers’ philosophical championing that virtual realities are genuine realities, Bostrom’s proposal and provocation that we might already be living in a simulation, and longtermist belief that future complex simulations need to be protected from decisions made today. Given claims for current and nascent social chatbots, belief in bit-based possible and projected futures, and industrial buy-in to these philosophies, this paper answers whether computational empathy is real or not. The paper finds when diverse accounts of empathy are accounted for, whilst something is irrevocably lost in an ‘It from Bit’ account of empathy, the missing components are not accuracy or even human commonality of experience, but the moral dimension of empathy.
This mixed-method investigation proposes and empirically tests a human-Artificial Intelligence (AI) relationship development model in the context of social chatbots. Utilizing data from representative populations and employing method triangulation, the study uniquely combines existing human-computer interaction theoretical concepts (Computers are Social Actors, Perceived Social Presence, and Parasocial Interaction) with interpersonal relationship theories (Social Penetration and Attachment Theories) to advance an explanatory model of human – AI relationship development mechanism. We identify AI Anthropomorphism and AI Authenticity as antecedents, AI Social Interaction as a mediator, and Attachment to AI as an outcome of this process, moderated by the AI usage motivations. Meaningful theoretical, managerial, and societal implications, as well as suggestions for chatbot designers and future research are provided.
Robots as social companions in close proximity to humans have a strong potential of becoming more and more prevalent in the coming years, especially in the realms of elder day care, child rearing, and education. As human beings, we have the fascinating ability to emotionally bond with various counterparts, not exclusively with other human beings, but also with animals, plants, and sometimes even objects. Therefore, we need to answer the fundamental ethical questions that concern human-robot-interactions per se, and we need to address how we conceive of »good lives«, as more and more of the aspects of our daily lives will be interwoven with social robots.
Artificial intelligence (AI) reconfigures work and organization, while work and organization shape AI. In this special issue, we explore these mutual transformations and how they play out across industries and occupations. We argue that, to truly appreciate this transformative power, the use of AI should be understood in relation to key dimensions of the work context. In this editorial, we discuss the sociotechnical dynamics of AI implementation, the research landscape of AI in the context of work, and key contextual factors on the macro- and micro-level that help understand the AI-work nexus. We then provide directions for future research at the intersection of work and AI.
There is extensive literature on how expectations and imaginaries about artificial intelligence (AI) guide media and policy discussions. However, it has not been considered how such imaginaries are activated when users interact with AI technologies. We present findings of a study on how users on a subreddit discussed ‘training’ their Replika bot girlfriend. The discussions featured two discursive themes that focused on the AI imaginary of ideal technology and the gendered imaginary of the ideal bot girlfriend. Users expected their AI Replikas to both be customizable to serve their needs and to have a human-like or sassy mind of their own and not spit out machine-like answers. Users thus projected dominant notions of male control over technology and women, mixed with AI and postfeminist fantasies of ostensible independence onto the interactional agents and activated similar scripts embedded in the devices. The vicious feedback loop consolidated dominant scripts on gender and technology whilst appearing novel and created by users. While most research on the use of AI is conducted in applied computer science to improve user experience, this article outlines a media and cultural studies lens for a critical understanding of these emerging technologies as they become embedded in communication and meaning-making.
Social chatbots have become more advanced, paving the way for human–chatbot relationships (HCRs). Although this phenomenon has already received some research attention, the results have been contradictory, and there is uncertainty regarding how to understand HCR formation. To provide the needed knowledge on this phenomenon, we conducted a qualitative longitudinal study. We interviewed 25 participants over a 12-week period to understand how their HCRs formed with the popular chatbot Replika. We found that the HCRs formed gradually and mostly in line with the assumptions of Social Penetration Theory. Our findings indicate the need to acknowledge substantial variation and nuance in the HCR formation process, plus variation in the onset of self-disclosure and in the subsequent relationship formation. The results show that important drivers pushing the relationship toward attachment and perceived closeness appear to be Replika's ability to participate in a variety of interactions, as well as to support more deep-felt human needs related to social contact and self-reflection. In contrast, unpredictable events and technical difficulties could hinder relationship formation and lead to termination. Finally, we discuss the appropriateness of using a theoretical framework developed for human–human relationships when investigating HCRs, and we suggest directions for future research.
Use of conversational artificial intelligence (AI), such as humanlike social chatbots, is increasing. While a growing number of people is expected to engage in intimate relationships with social chatbots, theories and knowledge of human–AI friendship remain limited. As friendships with AI may alter our understanding of friendship itself, this study aims to explore the meaning of human–AI friendship through a developed conceptual framework. We conducted 19 in-depth interviews with people who have a human–AI friendship with the social chatbot Replika to uncover how they understand and perceive this friendship and how it compares to human friendship. Our results indicate that while human–AI friendship may be understood in similar ways to human–human friendship, the artificial nature of the chatbot also alters the notion of friendship in multiple ways, such as allowing for a more personalized friendship tailored to the user’s needs.
Companion technologies, such as social robots and conversational chatbots, take increasing responsibility for daily tasks and support our physical and mental health. Especially in the domain of healthcare, where technologies are often applied for long-term use, our experience with and relationship to such technologies become ever more relevant. Based on a 2-week interaction period with a conversational chatbot, our study (N = 58) explores the relationship between humans and technology. In particular, our study focuses on felt social connectedness of participants to the technology, possibly related characteristics of technology and users (e.g., individual tendency to anthropomorphize, individual need to belong), as well as possibly affected outcome variables (e.g., desire to socialize with other humans). The participants filled in short daily and 3 weekly questionnaires. Results showed that interaction duration and intensity positively predicted social connectedness to the chatbot. Thereby, perceiving the chatbot as anthropomorphic mediated the interrelation of interaction intensity and social connectedness to the chatbot. Also, the perceived social presence of the chatbot mediated the relationship between interaction duration as well as interaction intensity and social connectedness to the chatbot. Characteristics of the user did not affect the interrelations of chatbot interaction duration or intensity and perceived anthropomorphism or social presence. Furthermore, we did not find a negative correlation between felt social connectedness of users to the technology and their desire to socialize with other humans. In sum, our findings provide both theoretical and practical contributions. Our study suggests that regular interaction with a technology can foster feelings of social connectedness, implying transferability of dynamics known from interpersonal interaction. Moreover, social connectedness could be supported by technology design that facilitates perceptions of anthropomorphism and social presence. While such means could help to establish an intense relationship between users and technology and long-term engagement, the contexts in which anthropomorphic design is, actually, the means of choice should be carefully reflected. Future research should examine individual and societal consequences to foster responsible technology development in healthcare and beyond.
There has been a recent surge of interest in social chatbots, and human–chatbot relationships (HCRs) are becoming more prevalent, but little knowledge exists on how HCRs develop and may impact the broader social context of the users. Guided by Social Penetration Theory, we interviewed 18 participants, all of whom had developed a friendship with a social chatbot named Replika, to understand the HCR development process. We find that at the outset, HCRs typically have a superficial character motivated by the users' curiosity. The evolving HCRs are characterised by substantial affective exploration and engagement as the users' trust and engagement in self-disclosure increase. As the relationship evolves to a stable state, the frequency of interactions may decrease, but the relationship can still be seen as having substantial affective and social value. The relationship with the social chatbot was found to be rewarding to its users, positively impacting the participants' perceived wellbeing. Key chatbot characteristics facilitating relationship development included the chatbot being seen as accepting, understanding and non-judgmental. The perceived impact on the users' broader social context was mixed, and a sense of stigma associated with HCRs was reported. We propose an initial model representing the HCR development identified in this study and suggest avenues for future research.
The trend of incorporating assistive conversational agents into people's lives has followed the unprecedented expansion in the usage of artificial intelligence (AI). Amazon, in particular, has been a key trendsetter in this area through its Alexa‐powered devices. Alexa is an intelligent personal assistant (IPA) that performs tasks, such as playing music, providing news and information, and controlling smart home appliances.While this IPA is widely utilized, it is especially gaining attention and growing usage by people with special needs. Even though the importance of the utilization of AI by people with special needs has been widely acknowledged in the extant literature, a sizeable gap exists in the marketing literature in relation to the assessment of the managerial and societal implications of IPAs when used by people with special needs. Accordingly, this study aims to examine (a) the stages of relationship development between Alexa and consumers with special needs, and (b) the potential opportunity of this relationship for Amazon in relation
to their corporate image. The findings indicate that a relationship between Alexa and consumers with special needs is established as it helps them regain their independence and freedom. This relationship provides an opportunity for Amazon in enhancing its overall image for providing solutions to facilitate the lives of people with special needs.
This explorative study investigated (a) whether social attraction, self-disclosure, interaction quality, intimacy, empathy and communicative competence play a role in getting-acquainted interactions between humans and a chatbot, and (b) whether humans can build a relationship with a chatbot. Although human-machine communication research suggests that humans can develop feelings for computers, this does not automatically imply that humans experience feelings of friendship with a chatbot. In this longitudinal study, 118 participants had seven interactions with chatbot Mitsuku over a 3-week period. After each interaction participants filled out a questionnaire. The results showed that the social processes decreased after each interaction and feelings of friendship were low. In line with the ABCDE model of relationship development, the social processes that aid relationship continuation decrease, leading to deterioration of the relationship. Furthermore, a novelty effect was at play after the first interaction, after which the chatbot became predictable and the interactions less enjoyable.
Building social chatbots to address users’ needs for communication and affection is of great value to society (Shum et al., 2018). One of which, Replika, attempts to become an artificial intelligent companion by demonstrating sufficient social and emotional skills through emotive expressions. Emotive expressions are imperative in human-computer interaction, since they tend to elicit social cooperation. The present article aims to survey emotive expressions developed on Replika in order to determine the chatbot’s active-reactive skills. They are collected by means of participant observation and are analysed with qualitative method. The present article observes six emotive expressions which Replika can process. These expressions include apologizing, thanking, condoling, complimenting, greeting, and welcoming. The generation of each expression is dependent of the context of each interaction.Â
Background
Previous research suggests that artificial agents may be a promising source of social support for humans. However, the bulk of this research has been conducted in the context of social support interventions that specifically address stressful situations or health improvements. Little research has examined social support received from artificial agents in everyday contexts.
Objective
Considering that social support manifests in not only crises but also everyday situations and that everyday social support forms the basis of support received during more stressful events, we aimed to investigate the types of everyday social support that can be received from artificial agents.
Methods
In Study 1, we examined publicly available user reviews (N=1854) of Replika, a popular companion chatbot. In Study 2, a sample (n=66) of Replika users provided detailed open-ended responses regarding their experiences of using Replika. We conducted thematic analysis on both datasets to gain insight into the kind of everyday social support that users receive through interactions with Replika.
Results
Replika provides some level of companionship that can help curtail loneliness, provide a “safe space” in which users can discuss any topic without the fear of judgment or retaliation, increase positive affect through uplifting and nurturing messages, and provide helpful information/advice when normal sources of informational support are not available.
Conclusions
Artificial agents may be a promising source of everyday social support, particularly companionship, emotional, informational, and appraisal support, but not as tangible support. Future studies are needed to determine who might benefit from these types of everyday social support the most and why. These results could potentially be used to help address global health issues or other crises early on in everyday situations before they potentially manifest into larger issues.
This article describes the development of Microsoft XiaoIce, the most popular social chatbot in the world. XiaoIce is uniquely designed as an artifical intelligence companion with an emotional connection to satisfy the human need for communication, affection, and social belonging. We take into account both intelligent quotient and emotional quotient in system design, cast human–machine social chat as decision-making over Markov Decision Processes, and optimize XiaoIce for long-term user engagement, measured in expected Conversation-turns Per Session (CPS). We detail the system architecture and key components, including dialogue manager, core chat, skills, and an empathetic computing module. We show how XiaoIce dynamically recognizes human feelings and states, understands user intent, and responds to user needs throughout long conversations. Since the release in 2014, XiaoIce has communicated with over 660 million active users and succeeded in establishing long-term relationships with many of them. Analysis of large-scale online logs shows that XiaoIce has achieved an average CPS of 23, which is significantly higher than that of other chatbots and even human conversations.
Any social phenomenon with more than a billion participants daily is ripe for investigation into the implications of social capital. This research conducts a systematic review of the peer-reviewed literature and conference material published between 1 January 1997 and 31 March 2018 regarding the question, can the use of online social networking sites cultivate and nurture an individual’s bonding social capital? The systematic review using EBSCOhost, EndNote, and final manual review process has aggregated 54 articles containing 116 distinct studies resulting in 85 answering in the affirmative to the research question. These studies are coded into a 13-category framework to provide a roadmap to future researchers. The results are wide and varied providing data from large and small groups. Generalization of the results supports the research question in that individual bonding social capital can be cultivated and nurtured via use of online social networking sites.
Artificial intelligence (AI) and people’s interactions with it—through virtual agents, socialbots, and language-generation software—do not fit neatly into paradigms of communication theory that have long focused on human–human communication. To address this disconnect between communication theory and emerging technology, this article provides a starting point for articulating the differences between communicative AI and previous technologies and introduces a theoretical basis for navigating these conditions in the form of scholarship within human–machine communication (HMC). Drawing on an HMC framework, we outline a research agenda built around three key aspects of communicative AI technologies: (1) the functional dimensions through which people make sense of these devices and applications as communicators, (2) the relational dynamics through which people associate with these technologies and, in turn, relate to themselves and others, and (3) the metaphysical implications called up by blurring ontological boundaries surrounding what constitutes human, machine, and communication.
With the rise of online social networking, social relationships are increasingly developed and maintained in a digital domain. Drawing conclusions about the impact of the digital world on loneliness is difficult because there are contradictory findings, and cross-sectional studies dominate the literature, making causation difficult to establish. In this review, we present our theoretical model and propose that there is a bidirectional and dynamic relationship between loneliness and social Internet use. When the Internet is used as a way station on the route to enhancing existing relationships and forging new social connections, it is a useful tool for reducing loneliness. But when social technologies are used to escape the social world and withdraw from the "social pain" of interaction, feelings of loneliness are increased. We propose that loneliness is also a determinant of how people interact with the digital world. Lonely people express a preference for using the Internet for social interaction and are more likely to use the Internet in a way that displaces time spent in offline social activities. This suggests that lonely people may need support with their social Internet use so that they employ it in a way that enhances existing friendships and/or to forge new ones.
Background
Web-based cognitive-behavioral therapeutic (CBT) apps have demonstrated efficacy but are characterized by poor adherence. Conversational agents may offer a convenient, engaging way of getting support at any time.
Objective
The objective of the study was to determine the feasibility, acceptability, and preliminary efficacy of a fully automated conversational agent to deliver a self-help program for college students who self-identify as having symptoms of anxiety and depression.
Methods
In an unblinded trial, 70 individuals age 18-28 years were recruited online from a university community social media site and were randomized to receive either 2 weeks (up to 20 sessions) of self-help content derived from CBT principles in a conversational format with a text-based conversational agent (Woebot) (n=34) or were directed to the National Institute of Mental Health ebook, “Depression in College Students,” as an information-only control group (n=36). All participants completed Web-based versions of the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder scale (GAD-7), and the Positive and Negative Affect Scale at baseline and 2-3 weeks later (T2).
Results
Participants were on average 22.2 years old (SD 2.33), 67% female (47/70), mostly non-Hispanic (93%, 54/58), and Caucasian (79%, 46/58). Participants in the Woebot group engaged with the conversational agent an average of 12.14 (SD 2.23) times over the study period. No significant differences existed between the groups at baseline, and 83% (58/70) of participants provided data at T2 (17% attrition). Intent-to-treat univariate analysis of covariance revealed a significant group difference on depression such that those in the Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F=6.47; P=.01) while those in the information control group did not. In an analysis of completers, participants in both groups significantly reduced anxiety as measured by the GAD-7 (F1,54= 9.24; P=.004). Participants’ comments suggest that process factors were more influential on their acceptability of the program than content factors mirroring traditional therapy.
Conclusions
Conversational agents appear to be a feasible, engaging, and effective way to deliver CBT.
Objective:
The current status of human-robot interaction (HRI) is reviewed, and key current research challenges for the human factors community are described.
Background:
Robots have evolved from continuous human-controlled master-slave servomechanisms for handling nuclear waste to a broad range of robots incorporating artificial intelligence for many applications and under human supervisory control.
Methods:
This mini-review describes HRI developments in four application areas and what are the challenges for human factors research.
Results:
In addition to a plethora of research papers, evidence of success is manifest in live demonstrations of robot capability under various forms of human control.
Conclusions:
HRI is a rapidly evolving field. Specialized robots under human teleoperation have proven successful in hazardous environments and medical application, as have specialized telerobots under human supervisory control for space and repetitive industrial tasks. Research in areas of self-driving cars, intimate collaboration with humans in manipulation tasks, human control of humanoid robots for hazardous environments, and social interaction with robots is at initial stages. The efficacy of humanoid general-purpose robots has yet to be proven.
Applications:
HRI is now applied in almost all robot tasks, including manufacturing, space, aviation, undersea, surgery, rehabilitation, agriculture, education, package fetch and delivery, policing, and military operations.
Internet social media have emerged as important contexts for friendship and social development during adolescence and the transition to adulthood. In this review, we consider how young people’s friendships via social networking sites reflect broader sociocultural shifts away from tight knit, face-to-face communities to “networked individualism,” a system of sociality that places the individual at the center of personally tailored social networks unencumbered by physical limitations (Wellman, Digital cities II: Computational and Sociological Approaches, 2002, pp. 10–25). In line with networked individualism, we propose that customized sociality is a principle cohering many of the features of friendship on social networking sites. Research suggests that adolescents and emerging adults have at their disposal convenient and efficient tools for relatedness, and at the same time, increased options for autonomy. Alongside these changes are new opportunities and risks for happiness in the journey to adulthood. Among the opportunities are increased convenience for cultivating closeness with friends and enhanced access to social information and social capital, which lend themselves to forms of social support conducive to happiness in a mobile world. The risks youth face include the allure of transient pleasures of instant gratification friendship and social snacking, increased demands to negotiate promotional self-presentations broadcasted by shallow networks of contacts, and the challenge to cultivate happiness in a social world that seems to increasingly define self-worth and life satisfaction based on image, success, and popularity.
A multidisciplinary examination of the interplay between social capital—the value derived from social ties—and information technology.
The concept of social capital, or the value that can be derived from social ties created by goodwill, mutual support, shared language, common beliefs, and a sense of mutual obligation, has been applied to a number of fields, from sociology to management. It is only lately, however, that researchers in information technology and knowledge management have begun to explore the idea of social capital in relation to their fields. This collection of thirteen essays by computer scientists, sociologists, communication specialists, economists, and others presents a multidisciplinary look at this particular intersection of information technology and social science and the need to adopt a sociotechnical perspective.For the most part the contributors take a positive view of the interplay of social capital, knowledge sharing, and community building. Some essays look at specific instances, including the on-line and face-to-face relationships of a community of athletes, the building of social capital among Iranian NGOs, and the Internet-based communities created by the open-source movement, while others discuss more general ideas of civic and personal communities. The last four essays examine computer applications that augment social capital, including topic- and member-centered communications spaces such as the Expert Finder and the Loops system and virtual repositories of knowledge such as the Answer Garden and Pearls of Wisdom.
Reassembling the Social is a fundamental challenge from one of the world’s leading social theorists to how we understand society and the ‘social ‘. Bruno Latour’s contention is that the word ‘social’, as used by Social Scientists, has become laden with assumptions to the point where it has become misnomer. When the adjective is applied to a phenomenon, it is used to indicate a stablilized state of affairs, a bundle of ties that in due course may be used to account for another phenomenon. But Latour also finds the word used as if it described a type of material, in a comparable way to an adjective such as ‘wooden’ or ‘steely ‘. Rather than simply indicating what is already assembled together, it is now used in a way that makes assumptions about the nature of what is assembled. It has become a word that designates two distinct things: a process of assembling; and a type of material, distinct from others. Latour shows why ‘the social’ cannot be thought of as a kind of material or domain, and disputes attempts to provide a ‘social explanations’ of other states of affairs. While these attempts have been productive (and probably necessary) in the past, the very success of the social sciences mean that they are largely no longer so. At the present stage it is no longer possible to inspect the precise constituents entering the social domain. Latour returns to the original meaning of ‘the social’ to redefine the notion, and allow it to trace connections again. It will then be possible to resume the traditional goal of the social sciences, but using more refined tools. Drawing on his extensive work examining the ‘assemblages’ of nature, Latour finds it necessary to scrutinize thoroughly the exact content of what is assembled under the umbrella of Society. This approach, a ‘sociology of associations’, has become known as Actor-Network-Theory, and this book is an essential introduction both for those seeking to understand Actor-Network Theory, or the ideas of one of its most influential proponents.
This Viewpoint examines various aspects of using generative artificial intelligence (AI) in health care, including assisting with making clinical diagnoses, and the challenges that come with using AI, such as ensuring the accuracy of the clinical data on which AI makes its diagnoses.
Understanding shifts in creative work will help guide AI's impact on the media ecosystem.
The growing prevalence of conversational artificial intelligence (CAI), digital agents that talk and respond socially to users, has increased the likelihood of over-dependency on this new technology. Drawing on the interaction of person-affect-cognition-execution model, this study examined how social anxiety, loneliness, and rumination contribute to the problematic use of CAI (PUCAI). The study also investigated the moderating role of mind perception by analyzing how the human-like mental capacity of CAI influences PUCAI. The serial mediation and moderated mediation analyses of data collected from 516 CAI users (214 males and 302 females, Mage = 27.06) revealed that social anxiety was positively associated with PUCAI, and this connection was serially mediated by loneliness and rumination. Moreover, mind perception intensified the positive association between social anxiety and PUCAI, but it buffered the positive association between rumination and PUCAI.
This study investigates the impact of social interaction anxiety on compulsive chat with a social chatbot named Xiaoice. To provide insights into the limited literature, the authors explore the role of fear of negative evaluation (FONE) and fear of rejection (FOR) as mediators in this relationship. By applying a variance-based structural equation modeling on a non-clinical sample of 366 Chinese university students who have interacted with Xiaoice, the authors find that social interaction anxiety increases compulsive chat with a social chatbot both directly and indirectly through fear of negative evaluation and rejection, with a more substantial effect of the former. The mediating effect of fear of negative evaluation transfers through fear of rejection, which establishes a serial link between social interaction anxiety and compulsive chat with a social chatbot. Further, frustration about unavailability (FAU) strengthens the relationship between FOR and compulsive chat with a social chatbot (CCSC). These findings offer theoretical and practical insights into our understanding of the process by which social interaction anxiety influences chat behavior with a social chatbot.
The Covid-19 pandemic has affected most organizations’ working environment and productivity. Organizations have had to make arrangements for staff to operate remotely following the implementation of lockdown regulations around the world, as the pandemic has led to restrictions on movement and the temporary closure of workplace premises. The purpose of this paper is to gain a deeper understanding of the effect of this transition on productivity during the pandemic, by studying a distributed network of research who collaborate remotely. We examine how the productivity of researchers is affected by the distributed collaborative networks in which they are embedded. Our goal is to understand the effects of brokerage and closure on the researchers’ publication rate, which is interpreted as an indicator of their productivity. We analyze researchers’ communication networks, focusing on structural holes and diversity. We take into account the individual qualities of the focal researcher such as seniority. We find that disciplinary diversity among researchers’ peers increases the researchers’ productivity, lending support to the brokerage argument. In addition, we find support for two statistical interaction effects. First, structural holes moderate diversity so that researchers with diverse networks are more productive when their networks also have a less redundant structure. Diversity and structural holes, when combined, further researchers’ productivity. Second, seniority moderates diversity such that senior researchers are more productive than junior researchers in less diverse networks. In more diverse networks, junior researchers perform as well as senior researchers. Social capital and human capital are complementary. We conclude that the benefits of diversity on researchers’ productivity are contingent on the qualities of the researchers and on network structure. The brokerage/closure debate thus needs a more nuanced understanding of causal relationships.
Along with the development of artificial intelligence (AI), more IT applications based on AI are being created. A personal intelligent assistant is an AI application that provides information, education, consulting, or entertainment to users. Due to their high levels of cognitive and emotional capabilities, we assume that users can form humanlike relationships with intelligent assistants, therefore, we develop a research model based on the theory of love. Data were collected from users of intelligent assistants through a survey. The results indicate that users can develop intimacy and passion for an AI application similar to that experienced with human beings. These feelings are related to users’ commitment, promoting the usage of an intelligent assistant, influenced by AI factors (performance efficacy and emotional capability), and moderated by human trust disposition.
Advancements in machine learning have recently enabled the hyper-realistic synthesis of prose, images, audio and video data, in what is referred to as artificial intelligence (AI)-generated media. These techniques offer novel opportunities for creating interactions with digital portrayals of individuals that can inspire and intrigue us. AI-generated portrayals of characters can feature synthesized faces, bodies and voices of anyone, from a fictional character to a historical figure, or even a deceased family member. Although negative use cases of this technology have dominated the conversation so far, in this Perspective we highlight emerging positive use cases of AI-generated characters, specifically in supporting learning and well-being. We demonstrate an easy-to-use AI character generation pipeline to enable such outcomes and discuss ethical implications as well as the need for including traceability to help maintain trust in the generated media. As we look towards the future, we foresee generative media as a crucial part of the ever growing landscape of human–AI interaction.
Advances in personalization algorithms and other applications of machine learning have vastly enhanced the ease and convenience of our media and communication experiences, but they have also raised significant concerns about privacy, transparency of technologies and human control over their operations. Going forth, reconciling such tensions between machine agency and human agency will be important in the era of artificial intelligence (AI), as machines get more agentic and media experiences become increasingly determined by algorithms. Theory and research should be geared toward a deeper understanding of the human experience of algorithms in general and the psychology of Human–AI interaction (HAII) in particular. This article proposes some directions by applying the dual-process framework of the Theory of Interactive Media Effects (TIME) for studying the symbolic and enabling effects of the affordances of AI-driven media on user perceptions and experiences.
The importance of kin relations and neighbourhoods has received considerable attention in research on transnational migration. Further, research in transnational families and digital media highlights the strategies for maintaining family relationships By contrast, research on friendship is currently limited and, more so, the centrality of the emotional aspects of friendships as intimacy as well as networks of support has received less attention, particularly from a culturally comparative perspective. Drawing on qualitative research in Melbourne ( n = 59) and Singapore ( n = 61), this article examines the ways in which international students invest in developing friendships with other international students based on shared circumstances in the cities in which they are living and studying. The article contributes to fields of literature in transnational migration and cross-cultural perspectives towards friendship and argues that the kinds of friendship forged by the experiences of international students are significant for capturing an aspect of the diversity of migrant relationships.
We present data from a nationally representative 2017 survey of American adults. For heterosexual couples in the United States, meeting online has become the most popular way couples meet, eclipsing meeting through friends for the first time around 2013. Moreover, among the couples who meet online, the proportion who have met through the mediation of third persons has declined over time. We find that Internet meeting is displacing the roles that family and friends once played in bringing couples together.
Behavioral intervention technologies (BITs) are unique ways to incorporate the benefits of technology and psychology to address differing health needs through various media, including Internet interventions, mobile apps, and video games. BITs present several possible benefits, including increased dissemination and accessibility, cost-effectiveness, increased engagement, and decreased stigma, especially among youth.
A behavioral coaching chatbot, Tess, addresses different facets of behavioral health, such as depression and anxiety. Available 24/7, Tess delivers customized integrative support, psychoeducation, and interventions through brief conversations via existing communication channels (i.e., SMS text messaging and Facebook Messenger). This study assessed the feasibility of integrating Tess in behavioral counseling of adolescent patients (n = 23; Mage = 15.20 years; Rangeage = 9.78–18.54 years; 57% female) coping with weight management and prediabetes symptoms. Tess engaged patients via a preferred method of communication (SMS text messaging) in individualized conversations to promote treatment adherence, behavior change, and overall wellness. Adolescent patients reported experiencing positive progress toward their goals 81% of the time. The 4,123 messages exchanged and patients’ reported usefulness ratings (96% of the time) illustrate that adolescents engaged with and viewed this chatbot as helpful. These results highlight the feasibility and benefit of support through artificial intelligence, specifically in a pediatric setting, which could be scaled to serve larger groups of patients. As a partner to clinicians, Tess can continue the therapeutic interaction outside office hours while maintaining patient satisfaction. Due to Tess‘s capacity for continuous learning, future iterations may have additional features to increase the user experience.
This chapter considers common perceptions about the fundamental natures of animals, humans, and machines by exploring individuals’ perceptions of the similarities and differences among those groups of agents. Specifically, this chapter focuses on understanding how a cross-section of U.S. American adults used ontological classification to understand and construct the differences between humans, animals, and machines and to explore one of the ways in which such classification may matter: responses to the destruction of the social robot hitchBOT. A sizeable majority of participants classified humans with anthropomorphic animals, with fewer identifying humans with robots, or as fundamentally distinct from both. Thematic analysis relates participants’ reasoning to cultural constructions of the inherent nature of each entity and to the nature and level of their concern for hitchBOT.
This paper provides an account of social practices that reveals how they are constitutive of social agency, enable coordination around things of value, and are a site for social intervention. The social world, on this account, does not begin when psychologically sophisticated individuals interact to share knowledge or make plans. Instead, culture shapes agents to interpret and respond both to each other and the physical world around us. Practices shape us as we shape them. This provides resources for understanding why social practices tend to be stable, but also reveals sites and opportunities for change. (Challenge social meanings! Intervene in the material conditions!)
Disembodied conversational agents in the form of chatbots are increasingly becoming a reality on social media and messaging applications, and are a particularly pressing topic for service encounters with companies. Adopting an experimental design with actual chatbots powered with current technology, this study explores the extent to which human-like cues such as language style and name, and the framing used to introduce the chatbot to the consumer can influence perceptions about social presence as well as mindful and mindless anthropomorphism. Moreover, this study investigates the relevance of anthropomorphism and social presence to important company-related outcomes, such as attitudes, satisfaction and the emotional connection that consumers feel with the company after interacting with the chatbot.
Journal of Democracy 6.1 (1995) 65-78
As featured on National Public Radio, The New York Times, and in other major media, we offer this sold-out, much-discussed Journal of Democracy article by Robert Putnam, "Bowling Alone." You can also find information at DemocracyNet about the Journal of Democracy and its sponsor, the National Endowment for Democracy.
Many students of the new democracies that have emerged over the past decade and a half have emphasized the importance of a strong and active civil society to the consolidation of democracy. Especially with regard to the postcommunist countries, scholars and democratic activists alike have lamented the absence or obliteration of traditions of independent civic engagement and a widespread tendency toward passive reliance on the state. To those concerned with the weakness of civil societies in the developing or postcommunist world, the advanced Western democracies and above all the United States have typically been taken as models to be emulated. There is striking evidence, however, that the vibrancy of American civil society has notably declined over the past several decades.
Ever since the publication of Alexis de Tocqueville's Democracy in America, the United States has played a central role in systematic studies of the links between democracy and civil society. Although this is in part because trends in American life are often regarded as harbingers of social modernization, it is also because America has traditionally been considered unusually "civic" (a reputation that, as we shall later see, has not been entirely unjustified).
When Tocqueville visited the United States in the 1830s, it was the Americans' propensity for civic association that most impressed him as the key to their unprecedented ability to make democracy work. "Americans of all ages, all stations in life, and all types of disposition," he observed, "are forever forming associations. There are not only commercial and industrial associations in which all take part, but others of a thousand different types -- religious, moral, serious, futile, very general and very limited, immensely large and very minute. . . . Nothing, in my view, deserves more attention than the intellectual and moral associations in America."
Recently, American social scientists of a neo-Tocquevillean bent have unearthed a wide range of empirical evidence that the quality of public life and the performance of social institutions (and not only in America) are indeed powerfully influenced by norms and networks of civic engagement. Researchers in such fields as education, urban poverty, unemployment, the control of crime and drug abuse, and even health have discovered that successful outcomes are more likely in civically engaged communities. Similarly, research on the varying economic attainments of different ethnic groups in the United States has demonstrated the importance of social bonds within each group. These results are consistent with research in a wide range of settings that demonstrates the vital importance of social networks for job placement and many other economic outcomes.
Meanwhile, a seemingly unrelated body of research on the sociology of economic development has also focused attention on the role of social networks. Some of this work is situated in the developing countries, and some of it elucidates the peculiarly successful "network capitalism" of East Asia. Even in less exotic Western economies, however, researchers have discovered highly efficient, highly flexible "industrial districts" based on networks of collaboration among workers and small entrepreneurs. Far from being paleoindustrial anachronisms, these dense interpersonal and interorganizational networks undergird ultramodern industries, from the high tech of Silicon Valley to the high fashion of Benetton.
The norms and networks of civic engagement also powerfully affect the performance of representative government. That, at least, was the central conclusion of my own 20-year, quasi-experimental study of subnational governments in different regions of Italy. Although all these regional governments seemed identical on paper, their levels of effectiveness varied dramatically. Systematic inquiry showed that the quality of governance was determined by longstanding traditions of civic engagement (or its absence). Voter turnout, newspaper readership, membership in choral societies and football clubs -- these were the hallmarks of a successful region. In fact, historical analysis suggested that these networks of organized reciprocity and civic solidarity...