This study examines public perceptions of AI fairness across three societal contexts in the U.S.: personal life, work life, and public life. AI fairness is conceptualized through perceived harms and benefits, offering a nuanced perspective on how individuals assess AI's impact on themselves versus others. Findings show that AI is generally perceived as more beneficial in personal and work contexts, while public life elicits greater skepticism and concern about harm. Women consistently perceive AI as less beneficial and more harmful across all contexts compared to men, reflecting broader gendered concerns about algorithmic decision-making Ethnicity differences also emerge, with Hispanic and Other minority groups (including Asian, Indigenous, and Native American individuals) reporting higher perceived benefits from AI. Conversely, White and Black participants are more likely to view AI as harmful across the various societal contexts. These disparities highlight the need for AI development and policy frameworks that account for demographic differences in perception to ensure equitable access and benefits across diverse populations. By integrating public perspectives into AI governance, this study aims to show how inclusive AI design can mitigate systemic bias, improve accessibility, and ensure AI-driven opportunities are equitably distributed rather than deepening technological disparities. .

The present study explores people’s attitudes towards an assortment of occupations on high and low-likelihood of automation probability. An omnibus survey (N = 1150) was conducted to measure attitudes about various emerging technologies, as well as demographic and individual traits. The results showed that respondents were not very comfortable with AI’s management across domains. To some degree, levels of comfort corresponded with the likelihood of automation probability, though some domains diverged from this pattern. Demographic traits explained the most variance in comfort with AI revealing that men and those with higher perceived technology competence were more comfortable with AI management in every domain. With the exception of personal assistance, those with lower internal locus of control were more comfortable with AI managing in almost every domain. Age, education, and employment showed little influence on comfort levels. The present study demonstrates a more holistic approach of assessing attitudes toward AI management at work. By incorporating demographic and self-efficacy variables, our research revealed that AI systems are perceived differently compared to other recent technological innovations.

The discussion and debates surrounding the robot rights topic demonstrate vast differences in the possible philosophical, ethical, and legal approaches to this question. Without top-down guidance of mutually agreed upon legal and moral imperatives, the public's attitudes should be an important component of the discussion. However, few studies have been conducted on how the general population views aspects of robot rights. The aim of the current study is to provide a new measurement that may facilitate such research. A Robot Rights and Responsibilities (RRR) scale is developed and tested. An exploratory factor analysis reveals a multi-dimensional construct with three factors-robots' rights, responsibilities, and capabilities-which are found to concur with theoretically relevant metrics. The RRR scale is contextualized in the ongoing discourse about the legal and moral standing of non-human and artificial entities. Implications for people's ontological perceptions of machines and suggestions for future empirical research are considered.

Modern AI applications have caused broad societal implications across key public domains. While previous research primarily focuses on individual user perspectives regarding AI systems, this study expands our understanding to encompass general public perceptions. Through a survey (N = 1506), we examined public trust across various tasks within education, healthcare, and creative arts domains. The results show that participants vary in their trust across domains. Notably, AI systems’ abilities were evaluated higher than their benevolence across all domains. Demographic traits had less influence on trust in AI abilities and benevolence compared to technology-related factors. Specifically, participants with greater technological competence, AI familiarity, and knowledge viewed AI as more capable in all domains. These participants also perceived greater systems’ benevolence in healthcare and creative arts but not in education. We discuss the importance of considering public trust and its determinants in AI adoption.

As biometric technology relies on bodily, physical information, it is among the more intrusive technologies in the contemporary consumer market. Consumer products containing biometric technology are becoming more popular and normalized, yet little is known about public perceptions concerning its privacy implications, especially from the perspective of human agency. This study examines how people perceive biometric technologies in different societal contexts and via different agents in control. Our study revealed that, in large part, people's perceptions of biometric technology are context-dependent, based on who retrieves and who benefits from the information and the situation where the data are collected. Participants were much more comfortable with more intrusive biometric technology in airport security than in a grocery store, and if it was employed to improve their health. We conclude by considering the implications of the survey for new threats to personal privacy that arise out of emerging technologies.

Education technology (Edtech) is a booming industry based on its potential to transform education and learning outcomes. With concern over remote learning, there is renewed excitement about the visual component of Edtech, namely VR, along with artificial intelligence (AI), resulting in more significant investments and innovations. Despite industrial-scale investment in Edtech's diffusion, less is known about the public's view. The public's reception of these technologies, though, maybe necessary in determining the contours of their eventual utilization. Therefore, we conducted a mixed-methods analysis based on a survey of a representative sample of the US population (N=2,254) that explores perceptions of Edtech in two instantiations: AI and VR in education. Respondents were more accepting of VR as a teaching tool than AI taking on educational roles. Assistive AI was born over AI with decision-making responsibilities. Personality and experiential traits had an influence on respondents' openness to education technologies. The results suggest support for a blended model of AI and VR use in the classroom.

Cet article analyse les changements structurels découlant de l’utilisation tout à la fois généralisée et tout au long de la vie des applications de la technologie des communications mobiles. Ces changements affectent tous les secteurs de la société, y compris la sphère domestique, qui a sans doute été la plus profondément touchée. Nous discutons des développements récents et délimitons les controverses concernant la portée appropriée des interactions entre la sphère privée et la sphère publique, de la collecte des données et des services interpersonnels. Nous examinons également les phénomènes psychologiques et sociologiques résultant de leur utilisation dans les interactions humaines et sociales. Enfin, nous explorons les implications à long terme de ces changements.

This chapter analyzes structural changes arising from both economy‐wide and life course-long applications of mobile communication technology. These changes trickle through all sectors of society, including the domestic sphere, which arguably has been most profoundly affected by it. The chapter highlights some ways in which people have used the mobile phone and its attendant technologies (e.g., social media) to emotional ends and speculates on what may be forthcoming as socially intelligent devices proliferate in people’s everyday lives and relationships. The authors discuss recent developments and delineate controversies concerning the appropriate scope of private and public interaction, data collection, and interpersonal services. They also examine psychological and sociological phenomena arising from their use in human life and social interactions. Finally, the authors explore the longer-term implications of these changes.

Artificial intelligence (AI) and robotics applications have proliferated primarily in the industrial sphere, and social scientific studies emphasized robots’ functionality and appropriateness for certain roles, especially those related to work and most particularly to robots replacing humans’ jobs. Notably, robot studies are often premised on negative prognostications, emphasizing how robots threaten livelihoods and are disruptive. As AI and robot technologies advance, however, more positive possibilities arise for robots’ social integration. However, there is an ontological divide between humans and machines that will likely influence people’s responses to and interactions with these emerging technologies. People may logically know and intend to treat robots as mere technological tools, but reflexively respond to them socially. This study explores these possible dynamics and examines how people perceive robots as social and human-like entities. A qualitative analysis of open-ended comments (N=591) collected through a survey on robot perceptions was conducted. Five main themes about social life with robots were revealed: robot as tool/machine (32.5% of comments), human-robot relationships (26.9%), social adjustment (19.0%), robot rights (10.7%), and robots’ aliveness/appearance (10.0%). The findings show that human-robot ontology is an important consideration for robots’ social acceptance and integration. AI-supported robotic technology presents great promise; however, its advancement challenges the ontological divide that has implications not only for human-machine interaction but also for self-identity and ultimately human-human relations. In terms of suggestions, these dynamics should be explored in tandem with ways to improve human-machine communication and considered from usability and design standpoints.